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• Basic Concepts.- MOP Evolutionary Algorithm Approaches.- MOEA Local Search and Coevolution.- MOEA Test Suites.- MOEA Testing and Analysis.- MOEA Theory and Issues.- Applications.- MOEA Parallelization.- Multi-Criteria Decision Making.- Alternative Metaheuristics.
• (source: Nielsen Book Data)

This textbook is a second edition of Evolutionary Algorithms for Solving Multi-Objective Problems, significantly expanded and adapted for the classroom. The various features of multi-objective evolutionary algorithms are presented here in an innovative and student-friendly fashion, incorporating state-of-the-art research. The book disseminates the application of evolutionary algorithm techniques to a variety of practical problems. It contains exhaustive appendices, index and bibliography and links to a complete set of teaching tutorials, exercises and solutions.
(source: Nielsen Book Data)

• Basic Concepts.- MOP Evolutionary Algorithm Approaches.- MOEA Local Search and Coevolution.- MOEA Test Suites.- MOEA Testing and Analysis.- MOEA Theory and Issues.- Applications.- MOEA Parallelization.- Multi-Criteria Decision Making.- Alternative Metaheuristics.
• (source: Nielsen Book Data)

This textbook is a second edition of Evolutionary Algorithms for Solving Multi-Objective Problems, significantly expanded and adapted for the classroom. The various features of multi-objective evolutionary algorithms are presented here in an innovative and student-friendly fashion, incorporating state-of-the-art research. The book disseminates the application of evolutionary algorithm techniques to a variety of practical problems. It contains exhaustive appendices, index and bibliography and links to a complete set of teaching tutorials, exercises and solutions.
(source: Nielsen Book Data)

• Robotics and Computer Vision
• Motion Planning for Car-Like Robots Using Lazy Probabilistic Roadmap Method
• A Vision System for Environment Representation: From Landscapes to Landmarks
• Adapting the Messy Genetic Algorithm for Path Planning in Redundant and Non-redundant Manipulators
• Navigation Advice from pq-Histograms
• Path Planning Using a Single-Query Bi-directional Lazy Collision Checking Planner
• An Exploration Approach for Indoor Mobile Robots Reducing Odometric Errors
• Feature Matching Using Accumulation Spaces
• On Selecting an Appropriate Colour Space for Skin Detection
• Heuristic Search and Optimization
• A Methodology for the Statistical Characterization of Genetic Algorithms
• MPSA: A Methodology to Parallelize Simulated Annealing and Its Application to the Traveling Salesman Problem
• A Cultural Algorithm for Constrained Optimization
• Penalty Function Methods for Constrained Optimization with Genetic Algorithms: A Statistical Analysis
• Automatic Generation of Control Parameters for the Threshold Accepting Algorithm
• Genetic Algorithms and Case-Based Reasoning as a Discovery and Learning Machine in the Optimization of Combinational Logic Circuits
• Speech Recognition and Natural Language
• Time-Domain Segmentation and Labelling of Speech with Fuzzy-Logic Post-Correction Rules
• IL MT System. Evaluation for Spanish-English Pronominal Anaphora Generation
• Out-of-Vocabulary Word Modeling and Rejection for Spanish Keyword Spotting Systems
• The DIME Project
• Detecting Deviations in Text Collections: An Approach Using Conceptual Graphs
• Using Long Queries in a Passage Retrieval System
• Logic
• Object-Oriented Constraint Programming with J. CP
• A Hybrid Treatment of Evolutionary Sets
• Games and Logics of Knowledge for Multi-agent Systems
• Modelling Learners of a Control Task with Inductive Logic Programming: A Case Study
• Simple Epistemic Logic for Relational Database
• Solving Optimal Location of Traffic Counting Points at Urban Intersections in CLP(FD)
• Flexible Agent Programming in Linear Logic
• Neural Networks
• Sample Complexity for Function Learning Tasks through Linear Neural Networks
• Extracting Knowledge from Artificial Neural Networks: An Empirical Comparison of Trepan and Symbolic Learning Algorithms
• Improving Pattern Recognition Using Several Feature Vectors
• Learning Optimization in a MLP Neural Network Applied to OCR
• Machine Learning
• Applications of a Collaborative Learning Ontology
• Automated Case Generation from Databases Using Similarity-Based Rough Approximation
• On the Stability of Example-Driven Learning Systems: A Case Study in Multirelational Learning
• Sharpe Ratio-Oriented Active Trading: A Learning Approach
• Multiagent Systems
• A Framework for Social Agents' Interaction Based on Communicative Action Theory and Dynamic Deontic Logic
• Autonomous Agents for Ubiquitous Collaborative Environments
• A Framework for Agent Based Network Management Systems
• A Multi-agent Cooperative Intelligent Tutoring System: The Case of Musical Harmony Domain
• Reacting to Unexpected Events and Communicating in Spite of Mixed Ontologies
• Logic of Interaction for Multiagent Systems
• Towards a Model for an Immune System
• Uncertainty Management
• A New Measure for the Accuracy of a Bayesian Network
• FINDS: A Training Package to Assess Forensic Fibre Evidence
• Predictive Control Based on an Auto-regressive Neuro-fuzzy Model Applied to the Steam Generator Startup Process at a Fossil Power Plant
• Modeling Dynamical Causal Interactions with Fuzzy Temporal Networks for Process Operation Support Systems
• AI Tools and Applications
• Resection Simulation with Local Tissue Deformations for Computer Assisted Surgery of the Prostate
• Biometrics and Data Mining: Comparison of Data Mining-Based Keystroke Dynamics Methods for Identity Verification
• Qualitative Systems Identication for Linear Time Invariant Dynamic Systems
• Monitoring the Execution of an Intelligent Planning System
• A Structural Model of ECA Rules in Active Database
• Use of a Rule-Based System for Process Control: Flow Instabilities in Axial Compressors Case Study
• A Distributed Event Service for Adaptive Group Awareness
• Mining Road Accidents
• An Inference Engine for Web Adaptive Cooperative Work
• Faults Diagnosis in Industrial Processes with a Hybrid Diagnostic System.

This book constitutes the refereed proceedings of the Second Mexican International Conference on Artificial Intelligence, MICAI 2002, held in Mrida, Yucatn, Mexico in April 2002. The 56 revised full papers presented were carefully reviewed and selected from more than 85 submissions from 17 countries. The papers are organized in topical sections on robotics and computer vision, heuristic search and optimization, speech recognition and natural language processing, logic, neural networks, machine learning, multi-agent systems, uncertainty management, and AI tools and applications.

• Face Recognition by Searching Most Similar Sample with Immune Learning
• A Multi-Objective Artificial Immune System Based on Hypervolume
• A Comparative Study of Negative Selection Based Anomaly Detection in Sequence Data
• Immune-Inspired Self Healing in Wireless Sensor Networks
• A Beginner's Guide to Systems Simulation in Immunology
• Clustering-Based Multi-objective Immune Optimization Evolutionary Algorithm
• Petri Nets Approach to Modeling of Immune System and Autism
• Mathematical Implementation of Interation between Malaria and Immune System
• Computing Longest Common Subsequences with the B-Cell Algorithm
• Bait a Trap: Introducing Natural Killer Cells to Artificial Immune System for Spyware Detection
• AC-CS: An Immune-Inspired Associative Classification Algorithm
• RC-DCA: A New Feature Selection and Signal Categorization Technique for the Dendritic Cell Algorithm Based on Rough Set Theory
• Artificial Immune Network Approach with Beta Differential Operator Applied to Optimization of Heat Exchangers
• A Negative Selection Approach to Intrusion Detection
• Clonal Expansion without Self-replicating Entities
• A Real Time Anomaly Detection System Based on Probabilistic Artificial Immune Systems
• CSA/IE: Novel Clonal Selection Algorithm with Information Exchange for High Dimensional Global Optimization Problems
• An Ecological Approach to Anomaly Detection: The EIA Model
• Rethinking Concepts of the Dendritic Cell Algorithm for Multiple Data Stream Analysis
• Stability-Based Model Selection for High Throughput Genomic Data: An Algorithmic Paradigm
• Towards an Evolutionary Procedure for Reverse-Engineering Biological Networks
• Distributed Computing with Prokaryotic Immune Systems
• The Immune System as a Metaphor for Topology Driven Patterns Formation in Complex Systems.

This book constitutes the refereed proceedings of the 11th International Conference on Artificial Immune Systems, ICARIS 2012, held in Taormia, Italy, in August 2012. The 19 revised selected papers presented were carefully reviewed and selected for inclusion in this book. In addition 4 papers of the workshop on bio and immune inspired algorithms and models for multi-level complex systems are included in this volume. Artificial immune systems (AIS) is a diverse and maturing area of research that bridges the disciplines of immunology, biology, medical science, computer science, physics, mathematics and engineering. The scope of AIS ranges from modelling and simulation of the immune system through to immune-inspired algorithms and in silico, in vitro and in vivo solutions.

• Convergence of the IGO-Flow of Isotropic Gaussian Distributions on Convex Quadratic Problems / Tobias Glasmachers
• Homogeneous and Heterogeneous Island Models for the Set Cover Problem / Andrea Mambrini, Dirk Sudholt and Xin Yao
• Geometric Semantic Genetic Programming / Alberto Moraglio, Krzysztof Krawiec and Colin G. Johnson
• Efficient Negative Selection Algorithms by Sampling and Approximate Counting / Johannes Textor
• Convergence of the Continuous Time Trajectories of Isotropic Evolution Strategies on Monotonic C22-composite Functions / Youhei Akimoto, Anne Auger and Nikolaus Hansen
• A Parameterized Runtime Analysis of Simple Evolutionary Algorithms for Makespan Scheduling / Andrew M. Sutton and Frank Neumann
• On Algorithm-Dependent Boundary Case Identification for Problem Classes / Chao Qian, Yang Yu and Zhi-Hua Zhou
• Cumulative Step-Size Adaptation on Linear Functions / Alexandre Chotard, Anne Auger and Nikolaus Hansen
• On the Behaviour of the (1, [lambda])-[sigma]SA-ES for a Constrained Linear Problem / Dirk V. Arnold
• An Empirical Evaluation of O(1) Steepest Descent for NK-Landscapes / Darrell Whitley, Wenxiang Chen and Adele Howe
• Experimental Supplements to the Computational Complexity Analysis of Genetic Programming for Problems Modelling Isolated Program Semantics / Tommaso Urli, Markus Wagner and Frank Neumann.

• ACO Beats EA on a Dynamic Pseudo-Boolean Function / Timo Kötzing and Hendrik Molter
• Runtime Analysis of Simple Interactive Evolutionary Biobjective Optimization Algorithms / Dimo Brockhoff, Manuel López-Ibáñez, Boris Naujoks and Günter Rudolph
• Parsimony Pressure versus Multi-objective Optimization for Variable Length Representations / Markus Wagner and Frank Neumann
• An Evolutionary and Graph-Based Method for Image Segmentation / Alessia Amelio and Clara Pizzuti
• Real-Time GPU Based Road Sign Detection and Classification / Roberto Ugolotti, Youssef S.G. Nashed and Stefano Cagnoni
• Acceleration of Evolutionary Image Filter Design Using Coevolution in Cartesian GP / Michaela Sikulova and Lukas Sekanina
• Transfer Learning, Soft Distance-Based Bias, and the Hierarchical BOA / Martin Pelikan, Mark W. Hauschild and Pier Luca Lanzi
• Reinforcement Learning with N-tuples on the Game Connect-4 / Markus Thill, Patrick Koch and Wolfgang Konen
• Efficient Sampling and Handling of Variance in Tuning Data Mining Models / Patrick Koch and Wolfgang Konen
• A Spatial EA Framework for Parallelizing Machine Learning Methods / Uday Kamath, Johan Kaers, Amarda Shehu and Kenneth A. De Jong
• Competing Mutating Agents for Bayesian Network Structure Learning / Olivier Regnier-Coudert and John McCall.

• A Meta-learning Prediction Model of Algorithm Performance for Continuous Optimization Problems / Mario A. Muñoz, Michael Kirley and Saman K. Halgamuge
• Pruning GP-Based Classifier Ensembles by Bayesian Networks / C. De Stefano, G. Folino, F. Fontanella and A. Scotto di Freca
• A Multi-parent Search Operator for Bayesian Network Building / David Iclănzan
• Enhancing Learning Capabilities by XCS with Best Action Mapping / Masaya Nakata, Pier Luca Lanzi and Keiki Takadama
• Using Expert Knowledge to Guide Covering and Mutation in a Michigan Style Learning Classifier System to Detect Epistasis and Heterogeneity / Ryan J. Urbanowicz, Delaney Granizo-Mackenzie and Jason H. Moore
• On Measures to Build Linkage Trees in LTGA / Peter A.N. Bosman and Dirk Thierens
• Evolvability Analysis of the Linkage Tree Genetic Algorithm / Dirk Thierens and Peter A.N. Bosman
• Alternative Restart Strategies for CMA-ES / Ilya Loshchilov, Marc Schoenauer and Michèle Sebag
• Are State-of-the-Art Fine-Tuning Algorithms Able to Detect a Dummy Parameter? / Elizabeth Montero, María-Cristina Riff, Leslie Pérez-Caceres and Carlos A. Coello Coello
• Compressed Network Complexity Search / Faustino Gomez, Jan Koutník and Jürgen Schmidhuber
• Single Node Genetic Programming on Problems with Side Effects / David Jackson.

• Generalized Compressed Network Search / Rupesh Kumar Srivastava, Jürgen Schmidhuber and Faustino Gomez
• Analyzing Module Usage in Grammatical Evolution / John Mark Swafford, Erik Hemberg, Michael O'Neill and Anthony Brabazon
• On the Anytime Behavior of IPOP-CMA-ES / Manuel López-Ibáñez, Tianjun Liao and Thomas Stützle
• HappyCat -- A Simple Function Class Where Well-Known Direct Search Algorithms Do Fail / Hans-Georg Beyer and Steffen Finck
• Differential Gene Expression with Tree-Adjunct Grammars / Eoin Murphy, Miguel Nicolau, Erik Hemberg, Michael O'Neill and Anthony Brabazon
• Analysing the Effects of Diverse Operators in a Genetic Programming System / MinHyeok Kim, Bob (RI) McKay, Kangil Kim and Xuan Hoai Nguyen
• Quantitative Analysis of Locally Geometric Semantic Crossover / Krzysztof Krawiec and Tomasz Pawlak
• Length Scale for Characterising Continuous Optimization Problems / Rachael Morgan and Marcus Gallagher
• Analyzing the Behaviour of Population-Based Algorithms Using Rayleigh Distribution / Gabriel Luque and Enrique Alba
• Variable Transformations in Estimation of Distribution Algorithms / Davide Cucci, Luigi Malagò and Matteo Matteucci.

• Controlling Overfitting in Symbolic Regression Based on a Bias/Variance Error Decomposition / Alexandros Agapitos, Anthony Brabazon and Michael O'Neill
• On Spectral Invariance of Randomized Hessian and Covariance Matrix Adaptation Schemes / Sebastian U. Stich and Christian L. Müller
• Variable Neighborhood Search and GRASP for Three-Layer Hierarchical Ring Network Design / Christian Schauer and Günther R. Raidl
• Extracting Key Gene Regulatory Dynamics for the Direct Control of Mechanical Systems / Jean Krohn and Denise Gorse
• An Evolutionary Optimization Approach for Bulk Material Blending Systems / Michael P. Cipold, Pradyumn Kumar Shukla, Claus C. Bachmann, Kaibin Bao and Hartmut Schmeck
• Study of Cancer Hallmarks Relevance Using a Cellular Automaton Tumor Growth Model / José Santos and Ángel Monteagudo
• Between Selfishness and Altruism: Fuzzy Nash-Berge-Zhukovskii Equilibrium / Réka Nagy, Noémi Gaskó, Rodica Ioana Lung and D. Dumitrescu
• A Spanning Tree-Based Encoding of the MAX CUT Problem for Evolutionary Search / Kisung Seo, Soohwan Hyun and Yong-Hyuk Kim
• A Hybrid Approach to Piecewise Modelling of Biochemical Systems / Zujian Wu, Shengxiang Yang and David Gilbert
• An Empirical Comparison of CMA-ES in Dynamic Environments / Chun-Kit Au and Ho-Fung Leung.

The two volume set LNCS 7491 and 7492 constitutes the refereed proceedings of the 12th International Conference on Parallel Problem Solving from Nature, PPSN 2012, held in Taormina, Sicily, Italy, in September 2012. The total of 105 revised full papers were carefully reviewed and selected from 226 submissions. The meeting began with 5 workshops which offered an ideal opportunity to explore specific topics in evolutionary computation, bio-inspired computing and metaheuristics. PPSN 2012 also included 8 tutorials. The papers are organized in topical sections on evolutionary computation; machine learning, classifier systems, image processing; experimental analysis, encoding, EDA, GP; multiobjective optimization; swarm intelligence, collective behavior, coevolution and robotics; memetic algorithms, hybridized techniques, meta and hyperheuristics; and applications.

• Temporal Evolution of Design Principles in Engineering Systems: Analogies with Human Evolution / Kalyanmoy Deb, Sunith Bandaru and Cem Celal Tutum
• Exploiting Prior Information in Multi-objective Route Planning / Antony Waldock and David W. Corne
• Analysis on Population Size and Neighborhood Recombination on Many-Objective Optimization / Naoya Kowatari, Akira Oyama, Hernán Aguirre and Kiyoshi Tanaka
• Clustering Criteria in Multiobjective Data Clustering / Julia Handl and Joshua Knowles
• Enhancing Profitability through Interpretability in Algorithmic Trading with a Multiobjective Evolutionary Fuzzy System / Adam Ghandar, Zbigniew Michalewicz and Ralf Zurbruegg
• Bootstrapping Aggregate Fitness Selection with Evolutionary Multi-Objective Optimization / Shlomo Israel and Amiram Moshaiov
• Network Topology Planning Using MOEA/D with Objective-Guided Operators / Wei Peng and Qingfu Zhang
• Elitist Archiving for Multi-Objective Evolutionary Algorithms: To Adapt or Not to Adapt / Hoang N. Luong and Peter A.N. Bosman
• An Improved Multiobjectivization Strategy for HP Model-Based Protein Structure Prediction / Mario Garza-Fabre, Eduardo Rodriguez-Tello and Gregorio Toscano-Pulido
• MOEA/D with Iterative Thresholding Algorithm for Sparse Optimization Problems / Hui Li, Xiaolei Su, Zongben Xu and Qingfu Zhang
• A Study on Evolutionary Multi-Objective Optimization with Fuzzy Approximation for Computational Expensive Problems / Alessandro G. Di Nuovo, Giuseppe Ascia and Vincenzo Catania.

• Multi-objective Optimization for Selecting and Scheduling Observations by Agile Earth Observing Satellites / Panwadee Tangpattanakul, Nicolas Jozefowiez and Pierre Lopez
• Tailoring [epsilon]-MOEA to Concept-Based Problems / Amiram Moshaiov and Yafit Snir
• Recombination of Similar Parents in SMS-EMOA on Many-Objective 0/1 Knapsack Problems / Hisao Ishibuchi, Naoya Akedo and Yusuke Nojima
• An Artificial Bee Colony Algorithm for the Unrelated Parallel Machines Scheduling Problem / Francisco J. Rodriguez, Carlos García-Martínez, Christian Blum and Manuel Lozano
• Controlling the Parameters of the Particle Swarm Optimization with a Self-Organized Criticality Model / Carlos M. Fernandes, Juan J. Merelo and Agostinho C. Rosa
• The Apiary Topology: Emergent Behavior in Communities of Particle Swarms / Andrew McNabb and Kevin Seppi
• ACO on Multiple GPUs with CUDA for Faster Solution of QAPs / Shigeyoshi Tsutsui
• It's Fate: A Self-organising Evolutionary Algorithm / Jan Bim, Giorgos Karafotias, S.K. Smit, A.E. Eiben and Evert Haasdijk
• Guide Objective Assisted Particle Swarm Optimization and Its Application to History Matching / Alan P. Reynolds, Asaad Abdollahzadeh, David W. Corne, Mike Christie and Brian Davies, et al.

• Animal Spirits in Population Spatial Dynamics / Matylda Jabłońska and Tuomo Kauranne
• Autonomous Shaping via Coevolutionary Selection of Training Experience / Marcin Szubert and Krzysztof Krawiec
• A Parallel Cooperative Co-evolutionary Genetic Algorithm for the Composite SaaS Placement Problem in Cloud Computing / Maolin Tang and Zeratul Izzah Mohd Yusoh
• Community Detection Using Cooperative Co-evolutionary Differential Evolution / Qiang Huang, Thomas White, Guanbo Jia, Mirco Musolesi and Nil Turan, et al.
• On-Line Evolution of Controllers for Aggregating Swarm Robots in Changing Environments / Berend Weel, Mark Hoogendoorn and A.E. Eiben
• Buildable Objects Revisited / Martin Waßmann and Karsten Weicker
• Collective Robot Navigation Using Diffusion Limited Aggregation / Jonathan Mullins, Bernd Meyer and Aiguo Patrick Hu
• Global Equilibrium Search Algorithms for Combinatorial Optimization Problems / Oleg Shylo, Dmytro Korenkevych and Panos M. Pardalos
• A Genetic Programming Approach for Evolving Highly-Competitive General Algorithms for Envelope Reduction in Sparse Matrices / Behrooz Koohestani and Riccardo Poli
• A Memetic Approach for the Max-Cut Problem / Qinghua Wu and Jin-Kao Hao
• An Improved Choice Function Heuristic Selection for Cross Domain Heuristic Search / John H. Drake, Ender Özcan and Edmund K. Burke.

• Optimizing Cellular Automata through a Meta-model Assisted Memetic Algorithm / Donato D'Ambrosio, Rocco Rongo, William Spataro and Giuseppe A. Trunfio
• A Memetic Algorithm for Community Detection in Complex Networks / Olivier Gach and Jin-Kao Hao
• Local Optima Networks, Landscape Autocorrelation and Heuristic Search Performance / Francisco Chicano, Fabio Daolio, Gabriela Ochoa, Sébastien Vérel and Marco Tomassini, et al.
• A Hyper-Heuristic Classifier for One Dimensional Bin Packing Problems: Improving Classification Accuracy by Attribute Evolution / Kevin Sim, Emma Hart and Ben Paechter
• A Framework to Hybridize PBIL and a Hyper-heuristic for Dynamic Environments / Gönül Uludağ, Berna Kiraz, A. Şima Etaner-Uyar and Ender Özcan
• Parallelization Strategies for Hybrid Metaheuristics Using a Single GPU and Multi-core Resources / Thé Van Luong, Eric Taillard, Nouredine Melab and El-Ghazali Talbi
• Adaptive Operator Selection at the Hyper-level / Eduardo Krempser, Álvaro Fialho and Helio J.C. Barbosa
• Improving Lin-Kernighan-Helsgaun with Crossover on Clustered Instances of the TSP / Doug Hains, Darrell Whitley and Adele Howe
• A Comparative Study of Three GPU-Based Metaheuristics / Youssef S.G. Nashed, Pablo Mesejo, Roberto Ugolotti, Jérémie Dubois-Lacoste and Stefano Cagnoni.

• The Effect of the Set of Low-Level Heuristics on the Performance of Selection Hyper-heuristics / M. Mısır, K. Verbeeck, P. De Causmaecker and G. Vanden Berghe
• Adaptive Evolutionary Algorithms and Extensions to the HyFlex Hyper-heuristic Framework / Gabriela Ochoa, James Walker, Matthew Hyde and Tim Curtois
• Applying Genetic Regulatory Networks to Index Trading / Miguel Nicolau, Michael O'Neill and Anthony Brabazon
• Evolutionary 3D-Shape Segmentation Using Satellite Seeds / Kai Engel and Heinrich Müller
• Benchmarking CHC on a New Application: The Software Project Scheduling Problem / Javier Matos and Enrique Alba
• Automatic Evaluation Methods in Evolutionary Music: An Example with Bossa Melodies / A.R.R. Freitas, F.G. Guimarães and R.V. Barbosa
• Efficient Discovery of Chromatography Equipment Sizing Strategies for Antibody Purification Processes Using Evolutionary Computing / Richard Allmendinger, Ana S. Simaria and Suzanne S. Farid
• Beware the Parameters: Estimation of Distribution Algorithms Applied to Circles in a Square Packing / Marcus Gallagher
• Block Diagonal Natural Evolution Strategies / Giuseppe Cuccu and Faustino Gomez
• Finding Good Affinity Patterns for Matchmaking Parties Assignment through Evolutionary Computation / Sho Kuroiwa, Keiichi Yasumoto, Yoshihiro Murata and Minoru Ito
• A Benchmark Generator for Dynamic Permutation-Encoded Problems / Michalis Mavrovouniotis, Shengxiang Yang and Xin Yao
• Evolving Femtocell Algorithms with Dynamic and Stationary Training Scenarios / Erik Hemberg, Lester Ho, Michael O'Neill and Holger Claussen.

The two volume set LNCS 7491 and 7492 constitutes the refereed proceedings of the 12th International Conference on Parallel Problem Solving from Nature, PPSN 2012, held in Taormina, Sicily, Italy, in September 2012. The total of 105 revised full papers were carefully reviewed and selected from 226 submissions. The meeting began with 6 workshops which offered an ideal opportunity to explore specific topics in evolutionary computation, bio-inspired computing and metaheuristics. PPSN 2012 also included 8 tutorials. The papers are organized in topical sections on evolutionary computation; machine learning, classifier systems, image processing; experimental analysis, encoding, EDA, GP; multiobjective optimization; swarm intelligence, collective behavior, coevolution and robotics; memetic algorithms, hybridized techniques, meta and hyperheuristics; and applications.
(source: Nielsen Book Data)

• Intro
• Title
• Preface
• Organization
• Table of Contents
• Main Track (Regular Papers)
• Multivariate Statistical Tests for Comparing Classification Algorithms
• Introduction
• Pairwise Comparison
• Univariate Case
• Multivariate Case
• Analysis of Variance
• Univariate Case
• Multivariate Case
• Experiments
• Setup
• Results
• Conclusions
• References
• Using Hyperheuristics under a GP Framework for Financial Forecasting
• Introduction
• Presentation of EDDIE 8
• Hyperheuristics Framework
• Heuristics and Operators
• The Framework
• Experimental Setup
• Results
• Conclusion
• References
• On the Effect of Connectedness for Biobjective Multiple and Long Path Problems
• Introduction
• Background
• Multiobjective Combinatorial Optimization
• Local Search and Connectedness
• The Single-Objective Long k-Path Problem
• The Biobjective Long k-Path Problem
• Definition
• Experimental Analysis
• The Biobjective Multiple k-Path Problem
• Definition
• Experimental Analysis
• Conclusions and Future Works
• References
• Improving Parallel Local Search for SAT
• Introduction
• Background
• The Propositional Satisfiability Problem
• Local Search for SAT
• Refinements
• Previous Work
• Complete Methods for Parallel SAT
• Incomplete Methods for Parallel SAT
• Cooperative Algorithms
• Knowledge Sharing in Parallel Local Search for SAT
• Using Best Known Configurations
• Weighting Best Known Configurations
• Restart Policy
• Experiments
• Experimental Settings
• Practical Performances with 4 Cores
• Practical Performances with 8 Cores
• Hardware Impact
• Conclusions and Future Work
• References
• Variable Neighborhood Search for the Time-Dependent Vehicle Routing Problem with Soft Time Windows
• Introduction
• Problem Description
• Solution Method
• Initial Solution
• Shaking.

• Local Search
• Acceptance Decision
• Computational Results
• Conclusion
• References
• Solving the Two-Dimensional Bin Packing Problem with a Probabilistic Multi-start Heuristic
• Introduction
• Organization of the Paper
• Related Work
• A New ILP Model
• The Proposed Algorithm
• Probabilistic LGFi
• Multi-start Algorithm
• Experimental Evaluation
• Problem Instances
• Parameter Setting
• Computational Results
• Conclusions
• References
• Genetic Diversity and Effective Crossover in Evolutionary Many-objective Optimization
• Introduction
• Analysis of Pareto Optimal Solutions in Many-objective 0/1 Knapsack Problem
• Mating Based on Proximity in Objective Space
• Related Works
• Local Recombination
• Controlling Crossed Genes for Crossover
• Problem of Local Recombination in MaOPs
• CCG for Two-Point Crossover (CCGTX)
• CCG for Uniform Crossover (CCGUX)
• Preparation
• Algorithms and Selection Methods
• Problems, Parameters and Metrics
• Experimental Results and Discussion
• Diversity of Genes in the Population Obtained by Conventional Crossover
• Effects of Local Recombination in MaOPs
• Effects of CCGTX in MaOPs
• Effects of CCGUX in MaOPs
• Conclusions
• References
• An Optimal Stopping Strategy for Online Calibration in Local Search
• Introduction
• The Bruss Algorithm
• The Estimation of the Probability of Success in Local Exploration
• Illustration of the Approach
• Experiments
• Conclusion and Future Work
• References
• Analyzing the Effect of Objective Correlation on the Efficient Set of MNK-Landscapes
• Introduction
• Background
• Multiobjective Combinatorial Optimization
• Metaheuristics for Multiobjective Combinatorial Optimization
• NK- and MNK-Landscapes
• MNK-Landscapes: Multiobjective NK-Landscapes with Correlation
• Definition
• Correlation between Objective Functions.

• Analysis of the Efficient Set Properties
• Cardinality of the Efficient Set
• Number of Supported Efficient Solutions
• Connectedness of the Efficient Set
• Discussion
• References
• Instance-Based Parameter Tuning via Search Trajectory Similarity Clustering
• Introduction
• Preliminaries
• Automated Parameter Configuration Problem
• One-Size-Fits-All Configurator
• Instance-Based Configurator
• Performance Metric
• Solution Approach
• Search Trajectory Similarity
• Search Trajectory Representation
• Similarity Calculation
• Clustering Method
• Training and Testing Phases
• Experimental Design
• Experiment Settings
• Validity and Statistical Significant Measurement
• Experimental Setup
• Empirical Evaluation
• Performance Comparison
• Comparison on Feature Selection
• Sensitivity Analysis on Different Initial Sequence Configurations
• Computational Results
• Discussion
• Conclusion and Future Works
• References
• Effective Probabilistic Stopping Rules for Randomized Metaheuristics: GRASP Implementations
• Introduction and Motivation
• GRASP and Experimental Environment
• Normal Approximation for GRASP Iterations
• Probabilistic Stopping Rule
• Concluding Remarks
• References
• A Classifier-Assisted Framework for Expensive Optimization Problems: A Knowledge-Mining Approach
• Introduction
• Background
• Expensive Optimization Problems
• Simulator Infeasible Vectors
• Proposed Framework
• The Model
• The Classifier
• The Framework
• Performance Analysis
• Test Problem and Benchmarks
• Knowledge-Mining the Classifier
• Summary
• References
• Robust Gaussian Process-Based Global Optimization Using a Fully Bayesian Expected Improvement Criterion
• Introduction
• Efficient Global Optimization
• The Expected Improvement Sampling Criterion for a Gaussian Process.

• Classical Parametrized Covariance Functions
• The EGO Algorithm
• The Case of Deceptive Functions
• Fully Bayesian One-Step Lookahead Optimization
• Student EI
• Numerical Experiments
• Optimization of a Deceptive Function
• Comparison on Sample Paths of a Gaussian Process
• References
• Hierarchical Hidden Conditional Random Fields for Information Extraction
• Introduction
• Hierarchical Hidden Conditional Random Fields
• Information Extraction
• Paper Organization
• HHMMs
• Representing an HHMM as a DBN
• HHCRFs
• Model
• Parameter Estimation
• Sentence Representation
• Hierarchical Models for Information Extraction
• Upper and Lower Levels
• Model Learning
• Inference
• Experiments
• Data
• Retrieved Results
• Performance Evaluation
• Results
• Conclusion
• References
• Solving Extremely Difficult MINLP Problems Using Adaptive Resolution Micro-GA with Tabu Search
• Introduction
• Related Work
• GAs for Solving MINLP Problems
• The Proposed Algorithm
• Variables Encoding and Genetic Operators
• Constraint Handling
• Micro GA
• Adaptive Resolution Approach
• Local Search
• Avoiding Redundancy
• Results
• Environment and Parameters
• Results and Discussion
• Conclusions and Future Work
• References
• Adaptive Abnormality Detection on ECG Signal by Utilizing FLAC Features
• Introduction
• Architecture of the Proposed Framework
• Preprocessing in Frequency Domain
• Local Auto-correlation on Complex Fourier Values (FLAC) for ECG
• Complex Subspace Method
• Experiments
• Conclusions
• References
• Gravitational Interactions Optimization
• Introduction
• Review GSA GIO and CSS
• Newton's Law of Universal Gravitation
• Gravitational Interactions Optimization
• Gravitational Interactions for Unimodal Optimization
• Gravitational Interactions for Multimodal Optimization
• Experiments.

• Test Functions
• Results
• Conclusions
• References
• On the Neutrality of Flowshop Scheduling Fitness Landscapes
• Motivations
• Background
• Definition of the Permutation Flowshop Scheduling Problem
• Neighborhood and Local Search
• Fitness Landscape
• Neutral Networks Analysis for the Permutation Flowshop Scheduling Problem
• Experimental Design
• Neutral Degree
• Typology of Neutral Networks
• Exploiting Neutrality to Solve the FSP
• Reaching Portals
• How to Guide the Search?
• Discussion
• References
• A Reinforcement Learning Approach for the Flexible Job Shop Scheduling Problem
• Introduction
• Flexible Job Shop Scheduling Problem
• Problem Formulation
• Previous Approaches
• Dispatching Rules
• Reinforcement Learning
• Q-Learning
• The Proposed Approach: Learning / Optimization
• Pseudo-code of the Algorithm
• Example
• Experimental Results
• Instances
• Parameters
• Comparative Study
• Conclusions and Future Work
• References
• Supervised Learning Linear Priority Dispatch Rules for Job-Shop Scheduling
• Introduction
• Priority Dispatch Rules for Job-Shop Scheduling
• Logistic Regression
• Experimental Study
• Data Generation
• Training Size and Accuracy
• Comparison with Single Priority Dispatching Rules
• Robustness towards Data Distributions
• Fixed Weights
• Summary and Conclusion
• References
• Fine-Tuning Algorithm Parameters Using the Design of Experiments Approach
• Introduction
• Automated Tuning Framework
• Screening Phase
• Exploration Phase
• Exploitation Phase
• Experimental Results
• Traveling Salesman Problem (TSP)
• Quadratic Assignment Problem (QAP)
• Conclusion
• References
• MetaHybrid: Combining Metamodels and Gradient-Based Techniques in a Hybrid Multi-Objective Genetic Algorithm
• GA Elements: Focus on Elitism
• SQP Elements: Focus on Constraints.

Annotation This book constitutes the thoroughly refereed post-conference proceedings of the 5th International Conference on Learning and Intelligent Optimization, LION 5, held in Rome, Italy, in January 2011. The 32 revised regular and 3 revised short papers were carefully reviewed and selected from a total of 99 submissions. In addition to the contributions to the general track there are 11 full papers and 3 short papers presented at the following four special sessions; IMON: Intelligent Multiobjective OptimizatioN, LION-PP: Performance Prediction Self* EAs: Self-tuning, self-configuring and self-generating evolutionary algorithms LION-SWAP: Software and Applications.

This book constitutes the refereed proceedings of the Second Mexican International Conference on Artificial Intelligence, MICAI 2002, held in Merida, Yucatan, Mexico in April 2002. The 56 revised full papers presented were carefully reviewed and selected from more than 85 submissions from 17 countries. The papers are organized in topical sections on robotics and computer vision, heuristic search and optimization, speech recognition and natural language processing, logic, neural networks, machine learning, multi-agent systems, uncertainty management, and AI tools and applications.
(source: Nielsen Book Data)

• An Introduction to Multi-Objective Evolutionary Algorithms and Their Applications
• Optimal Design of Industrial Electromagnetic Devices: A Multiobjective Evolutionary Approach
• Using a Particle Swarm Optimizer with a Multi-Objective Selection Scheme to Design Combinational Logic Circuits
• Automatic Control System Design via a Multiobjective Evolutionary Algorithm
• Evolutionary Multi-Objective Optimization of Trusses
• A Multi-Objective Evolutionary Algorithm for the Covering Tour Problem
• Multiobjective Aerodynamic Design and Visualization of Supersonic Wings by Using Adaptive Range Multiobjective Genetic Algorithms
• Mutli-Objective Spectroscopic Data Analysis of Inertial Confinement Fusion Implosion Cores: Plasma Gradient Determination
• On Machine Learning with Multiobjective Genetic Optimization
• and other papers.
• (source: Nielsen Book Data)

This book presents an extensive variety of multi-objective problems across diverse disciplines, along with statistical solutions using multi-objective evolutionary algorithms (MOEAs). The topics discussed serve to promote a wider understanding as well as the use of MOEAs, the aim being to find good solutions for high-dimensional real-world design applications. The book contains a large collection of MOEA applications from many researchers, and thus provides the practitioner with detailed algorithmic direction to achieve good results in their selected problem domain.
(source: Nielsen Book Data)

• An Introduction to Swarm Intelligence for Multi-objective Problems in Data Mining.- Multi--Criteria Ant Feature Selection Using Fuzzy Classifiers.- Multiobjective Particle Swarm Optimization in Classification-Rule Learning.- Using Multi-objective Particle Swarm Optimization for Designing Novel Classifiers.- Optimizing decision trees using multi-objective particle swarm optimization.- A Discrete Particle Swarm for Multi-objective Problems in Polynomial Neural Networks used for Classification: A Data Mining Perspective.- Rigorous Runtime Analysis of Swarm Intelligence Algorithms
• an Overview.- Mining Rules: A Parallel Multiobjective Particle Swarm Optimization Approach.- The Basic Principles of Metric Indexing.- Particle Evolutionary Swarm Multi-Objective Optimization for Vehicle Routing Problem with Time Windows.- Combining Correlated Data from Multiple Classifiers.
• (source: Nielsen Book Data)

The purpose of this book is to collect contributions that are at the intersection of multi-objective optimization, swarm intelligence (specifically, particle swarm optimization and ant colony optimization) and data mining. Such a collection intends to illustrate the potential of multi-objective swarm intelligence techniques in data mining, with the aim of motivating more researchers in evolutionary computation and machine learning to do research in this field. This volume consists of eleven chapters, including an introduction that provides the basic concepts of swarm intelligence techniques and a discussion of their use in data mining. Some of the research challenges that must be faced when using swarm intelligence techniques in data mining are also addressed. The rest of the chapters were contributed by leading researchers, and were organized according to the steps normally followed in Knowledge Discovery in Databases (KDD) (i.e., data preprocessing, data mining, and post processing). We hope that this book becomes a valuable reference for those wishing to do research on the use of multi-objective swarm intelligence techniques in data mining and knowledge discovery in databases.

• Novel swarm-based search methods.- Novel optimization algorithm.- Particle swarm optimization.- Ant colony optimization for travelling salesman problem.- Artificial bee colony algorithms.- Artificial immune system.- Evolutionary algorithms.- Neural networks and fuzzy methods.- Hybrid methods.- Multi-objective optimization.- Multi-agent systems.- Evolutionary clustering algorithms.- Classification methods.- GPU-based methods.- Scheduling and path planning.- Wireless sensor networks.- Power system optimization.- Swarm intelligence in image and video processing.- Applications of swarm intelligence to management problems.- Swarm intelligence for real-world application.
• (source: Nielsen Book Data)

This book and its companion volume, LNCS vol. 8794 and 8795 constitute the proceedings of the 5th International Conference on Swarm Intelligence, ICSI 2014, held in Hefei, China in October 2014. The 107 revised full papers presented were carefully reviewed and selected from 198 submissions. The papers are organized in 18 cohesive sections, 3 special sessions and one competitive session covering all major topics of swarm intelligence research and development such as novel swarm-based search methods; novel optimization algorithm; particle swarm optimization; ant colony optimization for travelling salesman problem; artificial bee colony algorithms; artificial immune system; evolutionary algorithms; neural networks and fuzzy methods; hybrid methods; multi-objective optimization; multi-agent systems; evolutionary clustering algorithms; classification methods; GPU-based methods; scheduling and path planning; wireless sensor networks; power system optimization; swarm intelligence in image and video processing; applications of swarm intelligence to management problems; swarm intelligence for real-world application.
(source: Nielsen Book Data)

• Novel swarm-based search methods.- Novel optimization algorithm.- Particle swarm optimization.- Ant colony optimization for travelling salesman problem.- Artificial bee colony algorithms.- Artificial immune system.- Evolutionary algorithms.- Neural networks and fuzzy methods.- Hybrid methods.- Multi-objective optimization.- Multi-agent systems.- Evolutionary clustering algorithms.- Classification methods.- GPU-based methods.- Scheduling and path planning.- Wireless sensor networks.- Power system optimization.- Swarm intelligence in image and video processing.- Applications of swarm intelligence to management problems.- Swarm intelligence for real-world application.
• (source: Nielsen Book Data)

This book and its companion volume, LNCS vol. 8794 and 8795 constitute the proceedings of the 5th International Conference on Swarm Intelligence, ICSI 2014, held in Hefei, China in October 2014. The 107 revised full papers presented were carefully reviewed and selected from 198 submissions. The papers are organized in 18 cohesive sections, 3 special sessions and one competitive session covering all major topics of swarm intelligence research and development such as novel swarm-based search methods; novel optimization algorithm; particle swarm optimization; ant colony optimization for travelling salesman problem; artificial bee colony algorithms; artificial immune system; evolutionary algorithms; neural networks and fuzzy methods; hybrid methods; multi-objective optimization; multi-agent systems; evolutionary clustering algorithms; classification methods; GPU-based methods; scheduling and path planning; wireless sensor networks; power system optimization; swarm intelligence in image and video processing; applications of swarm intelligence to management problems; swarm intelligence for real-world application.
(source: Nielsen Book Data)

• Multi-Objective Combinatorial Optimization: Problematic and Context.- Approximating Pareto-Optimal Sets Using Diversity Strategies in EvolutionaryMulti-Objective Optimization.- On the Velocity Update in Multi-objective Particle Swarm Optimizers.- Approaching Dynamic Multi-objective Optimization Problems by Using Parallel Evolutionary Algorithms.- ParadisEO-MOEO: A Software Framework for Evolutionary Multi-objective Optimization.- The Multiobjective Traveling Salesman Problem: A Survey and a New Approach.- On the Performance of Local Search for the Biobjective Traveling Salesman Problem.- A Bi-objectiveMetaheuristic for Disaster Relief Operation Planning.
• (source: Nielsen Book Data)

The purpose of this book is to collect contributions that deal with the use of nature inspired metaheuristics for solving multi-objective combinatorial optimization problems. Such a collection intends to provide an overview of the state-of-the-art developments in this field, with the aim of motivating more researchers in operations research, engineering, and computer science, to do research in this area. As such, this book is expected to become a valuable reference for those wishing to do research on the use of nature inspired metaheuristics for solving multi-objective combinatorial optimization problems.
(source: Nielsen Book Data)

This book constitutes the refereed proceedings of the Third International Conference on Evolutionary Multi-Criterion Optimization, EMO 2005, held in Guanajuato, Mexico, in March 2005. The 59 revised full papers presented together with 2 invited papers and the summary of a tutorial were carefully reviewed and selected from the 115 papers submitted. The papers are organized in topical sections on algorithm improvements, incorporation of preferences, performance analysis and comparison, uncertainty and noise, alternative methods, and applications in a broad variety of fields.
(source: Nielsen Book Data)

• An Introduction to Multi-Objective Evolutionary Algorithms and Their Applications
• Applications of Multi-Objective Evolutionary Algorithms in Engineering Design
• Optimal Design of Industrial Electromagnetic Devices: A Multiobjective Evolutionary Approach
• Groundwater Monitoring Design: A Case Study Combining Epsilon Dominance Archiving and Automatic Parameterization for the NSGA-II
• Using a Particle Swarm Optimizer with a Multi-Objective Selection Scheme to Design Combinational Logic Circuits
• Application of Multi-Objective Evolutionary Algorithms in Autonomous Vehicles Navigation
• Automatic Control System Design via a Multiobjective Evolutionary Algorithm
• The Use of Evolutionary Algorithms to Solve Practical Problems in Polymer Extrusion
• The Use of Evolutionary Algorithms to Solve Practical Problems in Polymer Extrusion
• City and Regional Planning via a MOEA: Lessons Learned
• A Multi-Objective Evolutionary Algorithm for the Covering Tour Problem
• A Computer Engineering Benchmark Application for Multiobjective Optimizers
• Multiobjective Aerodynamic Design and Visualization of Supersonic Wings by Using Adaptive Range Multiobjective Genetic Algorithms
• Applications of a Multi-Objective Genetic Algorithm in Chemical and Environmental Engineering
• Multi-Objective Spectroscopic Data Analysis of Inertial Confinement Fusion Implosion Cores: Plasma Gradient Determination
• Application of Multiobjective Evolutionary Optimization Algorithms in Medicine
• On Machine Learning with Multiobjective Genetic Optimization
• Generalized Analysis of Promoters: A Method for DNA Sequence Description
• Multi-Objective Evolutionary Algorithms for Computer Science Applications
• Design of Fluid Power System Using a Multi Objective Genetic Algorithm
• Elimination of Exceptional Elements in Cellular Manufacturing Systems Using Multi-Objective Genetic Algorithms
• Single-Objective and Multi-Objective Evolutionary Flowshop Scheduling
• Evolutionary Operators Based on Elite Solutions for Bi-Objective Combinatorial Optimization
• Multi-Objective Rectangular Packing Problem
• Multi-Objective Algorithms for Attribute Selection in Data Mining
• Financial Applications of Multi-Objective Evolutionary Algorithms: Recent Developments and Future Research Directions
• Evolutionary Multi-Objective Optimization Approach to Constructing Neural Network Ensembles for Regression
• Optimizing Forecast Model Complexity Using Multi-Objective Evolutionary Algorithms
• Even Flow Scheduling Problems in Forest Management
• Using Diversity to Guide the Search in Multi-Objective Optimization.

• Intro
• Preface
• About This Book
• Contents
• Editors and Contributors
• Scenario-Based Neural Network Model for Integrated Lighting Schemes in Residential Buildings
• 1 Introduction
• 2 Related Work
• 3 Background
• 4 Methodology
• 4.1 Data Gathering and Analysis
• 4.2 Simulation in DIALux
• 4.3 BPNN Model
• 5 Results
• 5.1 DIALux Model
• 5.2 BPNN Model
• 6 Conclusion
• References
• Electrical Muscle Stimulation Models Identification Based on Hammerstein Structure and Gravitational Search Algorithm
• 1 Introduction
• 2 Problem Description

• 3 EMS System Design Methodology
• 4 Simulation Results and Discussions
• 5 Conclusions
• References
• Experimental Analysis of ``A Novel Swarm Intelligence Optimization Approach: Sparrow Search Algorithm''
• 1 Introduction
• 2 Explanation of Sparrow Search Algorithm (SSA)
• 3 Flow Chart of Sparrow Search Algorithm (SSA)
• 4 Experimental Outcomes as Well as Analysis
• 4.1 Tuning of the Parameters and Environment of Experiment
• 4.2 Analysis From the Optimal Point of View
• 4.3 Analysis From Convergence Point of View
• 5 Conclusion
• References

• Solving FJSP Using Multi-agent System with GA
• 1 Introduction
• 2 Literature Review
• 3 The Problem Definition
• 4 The Architecture of Multi-agent System
• 5 Experimental Findings
• 6 Conclusion
• References
• A Comparative Analysis on Optimal Power Allocation and Pairing of User in Downlink NOMA System
• 1 Introduction
• 2 System Model
• 3 User Pairing Strategy and Optimal Power Allocation Technique
• 3.1 User Pairing Strategy
• 3.2 Power Allocation Technique
• 4 Results and Discussions
• 5 Conclusion
• References
• Sentiment-Based Community Detection Using Graph Transformation

• 1 Introduction
• 2 Related Work
• 2.1 Sentiment Analysis in Social Networks
• 2.2 Community Detection
• 3 Sentiment-Based Community Detection
• 3.1 Sentiment Analysis
• 3.2 Graph Generation
• 3.3 Graph Transformation
• 3.4 Community Detection
• 4 Experimental Setup and Results
• 5 Conclusion
• References
• Multivariate Data-Driven Approach to Identify Reliable Neural Components and Latency in a P300 Dataset Using Correlated Component Analysis
• 1 Introduction
• 2 Materials and Method
• 2.1 Preprocessing
• 2.2 Application of Correlated Component Analysis

• 2.3 Signal-to-Noise Ratio
• 2.4 Neural Activity Using the Forward Model Approach
• 3 Result
• 4 Discussion
• 5 Conclusion
• References
• A Framework for an Intelligent Voice-Assisted Language Translation System for Agriculture-Related Queries
• 1 Introduction
• 2 Background
• 3 Methodology
• 3.1 Kannada Speech Typing
• 3.2 Kannada Text to English Text Translation and Vice Versa
• 3.3 Kannada Text to Speech Conversion
• 4 Results
• 5 Analysis
• 5.1 Comparison with Other Translation Systems
• 6 Conclusion
• 7 Future Scope
• References

The volume is a collection of best selected research papers presented at International Conference on Advances in Data-driven Computing and Intelligent Systems (ADCIS 2022) held at BITS Pilani, K K Birla Goa Campus, Goa, India during 23 25 September 2022. It includes state-of-the art research work in the cutting-edge technologies in the field of data science and intelligent systems. The book presents data-driven computing; it is a new field of computational analysis which uses provided data to directly produce predictive outcomes. The book will be useful for academicians, research scholars, and industry persons

• Plenary Talks
• Interactive Approaches in Multiple Criteria Decision Making and Evolutionary Multi-objective Optimization
• Towards Automatically Configured Multi-objective Optimizers
• A Review of Evolutionary Multiobjective Optimization Applications in Aerospace Engineering
• Performance evaluation of multiobjective optimization algorithms: quality indicators and the attainment function
• Theory and Hyper-Heuristics
• A Multimodal Approach for Evolutionary Multi-objective Optimization (MEMO): Proof-of-Principle Results
• Unwanted Feature Interactions Between the Problem and Search Operators in Evolutionary Multi-objective Optimization
• Neutral but a Winner! How Neutrality helps Multiobjective Local Search Algorithms
• To DE or not to DE? Multi-Objective Differential Evolution Revisited from a Component-Wise Perspective
• Model-Based Multi-Objective Optimization: Taxonomy, Multi-Point Proposal, Toolbox and Benchmark
• Temporal Innovization: Evolution of Design Principles Using Multi-objective Optimization
• MOEA/D-HH: A Hyper-Heuristic for Multi-objective Problems
• Using hyper-heuristic to select leader and archiving methods for many-objective problems
• Algorithms
• Adaptive Reference Vector Generation for Inverse Model Based Evolutionary Multiobjective Optimization with Degenerate and Disconnected Pareto Fronts
• MOEA/PC: Multiobjective Evolutionary Algorithm Based on Polar Coordinates
• GD-MOEA: A New Multi-Objective Evolutionary Algorithm based on the Generational Distance Indicator
• Experiments on Local Search for Bi-objective Unconstrained Binary Quadratic Programming
• A Bug in the Multiobjective Optimizer IBEA: Salutary Lessons for Code Release and a Performance Re-Assessment
• A Knee-based EMO Algorithm with an Efficient Method to Update Mobile Reference Points
• A Hybrid Algorithm for Stochastic Multiobjective Programming Problem
• Parameter Tuning of MOEAs using a Bilevel Optimization Approach
• Pareto adaptive scalarising functions for decomposition based algorithms
• A bi-level multiobjective PSO algorithm
• An interactive simple indicator-based evolutionary algorithm (I-SIBEA) for multiobjective optimization problems
• Combining Non-dominance, Objective-sorted and Spread Metric to Extend Firefly Algorithm to Multi-objective Optimization
• GACO: a parallel evolutionary approach to multi-objective scheduling
• Kriging Surrogate Model Enhanced by Coordinate Transformation of Design Space Based on Eigenvalue Decomposition
• A Parallel Multi-Start NSGA II Algorithm for Multiobjective Energy Reduction Vehicle Routing Problem
• Evolutionary Inference of Attribute-based Access Control Policies
• Hybrid Dynamic Resampling for Guided Evolutionary Multi-Objective Optimization
• A Comparison of Decoding Strategies for the 0/1 Multi-objective Unit Commitment Problem
• Comparing Decomposition-based and Automatically Component-Wise Designed Multi-objective Evolutionary Algorithms
• Upper Confidence Bound (UCB) Algorithms for Adaptive Operator Selection in MOEA/D
• Towards Understanding Bilevel Multi-objective Optimization with Deterministic Lower Level Decisions.

This book constitutes the refereed proceedings of the 8th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2015 held in Guimarães, Portugal in March/April 2015. The 68 revised full papers presented together with 4 plenary talks were carefully reviewed and selected from 90 submissions. The EMO 2015 aims to continue these type of developments, being the papers presented focused in: theoretical aspects, algorithms development, many-objectives optimization, robustness and optimization under uncertainty, performance indicators, multiple criteria decision making and real-world applications.

• Many-Objectives Optimization, Performance and Robustness
• Evolutionary Many-objective Optimization based on Kuhn-Munkres? Algorithm
• A KKT Proximity Measure for Evolutionary Multi-Objective and Many-Objective Optimization
• U-NSGA-III: A Unified Evolutionary Optimization Procedure for Single, Multiple, and Many Objectives? Proof-of- Principle Results
• Clustering based parallel Many-objective Evolutionary Algorithms using the shape of the objective vectors
• Faster Exact Algorithms for Computing Expected Hypervolume Improvement
• A GPU-based Algorithm for a Faster Hypervolume Contribution Computation
• A Feature-based Performance Analysis in Evolutionary Multiobjective Optimization
• Modified Distance Calculation in Generational Distance and Inverted Generational Distance
• On the Behavior of Stochastic Local Search within Parameter Dependent MOPs
• An Evolutionary Approach to Active Robust Multiobjective Optimisation
• Linear scalarization Pareto front identification in stochastic environments
• Elite Accumulative Sampling Strategies for Noisy Multi-Objective Optimisation
• Guideline Identification for Optimization under Uncertainty through the Optimization of a Boomerang Trajectory
• MCDM
• Using indifference information in robust ordinal regression
• A Multi-objective genetic algorithm for inferring inter-criteria parameters for water supply consensus
• Genetic Algorithm Approach for a Class of Multi-criteria, Multi-vehicle Planner of UAVs
• An Interactive Evolutionary Multiobjective Optimization Method: Interactive WASF-GA
• On Generalizing Lipschitz Global Methods for Multiobjective Optimization
• Dealing with scarce optimization time in complex logistics optimization: A study on the biobjective Swap-Body Inventory Routing Problem
• Machine Decision Makers as a Laboratory for Interactive EMO
• Real World Applications
• Aircraft Air Inlet Design Optimization via Surrogate-Assisted Evolutionary Computation
• Diesel Engine Drive-Cycle Optimization with the Integrated Optimization Environment? Liger
• Re-design for robustness: An approach based on many objective optimization
• A Model for a Human Decision-Maker in a Polymer Extrusion Process
• Multi-Objective Optimization of Gate Location and Processing Conditions in Injection Molding Using MOEAs: Experimental Assessment
• A Multi-Criteria Decision Support System for a Routing Problem in Waste Collection
• Application of Evolutionary Multiobjective Algorithms for solving the problem of Energy Dispatch in Hydroelectric Power Plants
• Solutions in Under 10 Seconds for Vehicle Routing Problems with Time Windows using Commodity Computers
• A comparative study of algorithms for solving the Multiobjective Open-Pit Mining Operational Planning Problems
• A Model to Select a Portfolio of Multiple Spare Parts for a Public Bus Transport Service Using NSGA II.-A Multi-Objective Optimization Approach Associated to Climate Change Analysis to Improve Systematic Conservation Planning
• Marginalization in Mexico: An Application of the Electre III[Pleaseinsertintopreamble]MOEA Methodology
• Integrating Hierarchical Clustering and Pareto-Efficacy to Preventive Controls Selection in Voltage Stability Assessment
• Multi-objective Evolutionary Algorithm with Discrete Differential Mutation Operator for Service Restoration in Largescale
• Distribution Systems
• Combining Data Mining and Evolutionary Computation for Multi-Criteria Optimization of Earthworks
• Exploration of Two-Objective Scenarios on Supervised Evolutionary Feature Selection: a Survey and a Case Study
• (Application to Music Categorisation)
• A Multi-Objective Approach for Building Hyperspectral Remote Sensed Image Classifier Combiners
• Multi-Objective Optimization of Barrier Coverage with Wireless Sensors
• Comparison of Single and Multi-objective Evolutionary Algorithms for Robust Link-state Routing.

This book constitutes the refereed proceedings of the 8th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2015 held in Guimarães, Portugal in March/April 2015. The 68 revised full papers presented together with 4 plenary talks were carefully reviewed and selected from 90 submissions. The EMO 2015 aims to continue these type of developments, being the papers presented focused in: theoretical aspects, algorithms development, many-objectives optimization, robustness and optimization under uncertainty, performance indicators, multiple criteria decision making and real-world applications.