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• Optimum Tracking in Dynamic Environments.- Explicit Memory Schemes for Evolutionary Algorithms in Dynamic Environments.- Particle Swarm Optimization in Dynamic Environments.- Evolution Strategies in Dynamic Environments.- Orthogonal Dynamic Hill Climbing Algorithm: ODHC.- Genetic Algorithms with Self-Organizing Behaviour in Dynamic Environments.- Learning and Anticipation in Online Dynamic Optimization.- Evolutionary Online Data Mining: An Investigation in a Dynamic Environment.- Adaptive Business Intelligence: Three Case Studies.- Evolutionary Algorithms for Combinatorial Problems in the Uncertain Environment of the Wireless Sensor Networks.- Approximation of Fitness Functions.- Individual-based Management of Meta-models for Evolutionary Optimization with Application to Three-Dimensional Blade Optimization.- Evolutionary Shape Optimization Using Gaussian Processes.- A Study of Techniques to Improve the Efficiency of a Multi-Objective Particle Swarm Optimizer.- An Evolutionary Multi-objective Adaptive Meta-modeling Procedure Using Artificial Neural Networks.- Surrogate Model-Based Optimization Framework: A Case Study in Aerospace Design.- Handling Noisy Fitness Functions.- Hierarchical Evolutionary Algorithms and Noise Compensation via Adaptation.- Evolving Multi Rover Systems in Dynamic and Noisy Environments.- A Memetic Algorithm Using a Trust-Region Derivative-Free Optimization with Quadratic Modelling for Optimization of Expensive and Noisy Black-box Functions.- Genetic Algorithm to Optimize Fitness Function with Sampling Error and its Application to Financial Optimization Problem.- Search for Robust Solutions.- Single/Multi-objective Inverse Robust Evolutionary Design Methodology in the Presence of Uncertainty.- Evolving the Tradeoffs between Pareto-Optimality and Robustness in Multi-Objective Evolutionary Algorithms.- Evolutionary Robust Design of Analog Filters Using Genetic Programming.- Robust Salting Route Optimization Using Evolutionary Algorithms.- An Evolutionary Approach For Robust Layout Synthesis of MEMS.- A Hybrid Approach Based on Evolutionary Strategies and Interval Arithmetic to Perform Robust Designs.- An Evolutionary Approach for Assessing the Degree of Robustness of Solutions to Multi-Objective Models.- Deterministic Robust Optimal Design Based on Standard Crowding Genetic Algorithm.
• (source: Nielsen Book Data)

This book compiles recent advances of evolutionary algorithms in dynamic and uncertain environments within a unified framework. The book is motivated by the fact that some degree of uncertainty is inevitable in characterizing any realistic engineering systems. Discussion includes representative methods for addressing major sources of uncertainties in evolutionary computation, including handle of noisy fitness functions, use of approximate fitness functions, search for robust solutions, and tracking moving optimums.
(source: Nielsen Book Data)

• Introduction
• Reconfigurable Hardware
• Evolutionary Algorithms
• Evolvable Hardware
• Towards Evolvable Components
• Evolvable Computational Machines
• An Evolvable Component for Image-Pre-processing
• Virtual Reconfigurable Devices
• Concluding Statements
• References
• Index.
• (source: Nielsen Book Data)

This book deals with adaptive evolvable systems operating in a changing environment. The proposed component approach for the design of evolvable systems, the mathematical theory of evolvable machines, and the idea of virtual reconfigurable circuits have opened a way towards a better understanding of evolvable systems and to the design of more adaptive, competitive and innovative engineering products. In particular, topics such as the computational power of evolvable systems, the design of complex human-competitive digital circuits and the implementation of a one-clock partial reconfiguration in a common field-programmable gate array are introduced, investigated and implemented. The application domain is adaptive image preprocessing.
(source: Nielsen Book Data)

• [I]. Engineering and real-world applications: 1. A comparison of different many-objective optimization algorithms for energy system optimization / Tobias Rodemann
• 2. Design of powered floor systems for mobile robots with differential evolution / Eric Medvet, Stefano Seriani, Alberto Bartoli, and Paolo Gallina
• 3. Solving the multi-objective flexible job-shop scheduling problem with alternative recipes for a chemical production process / Piotr Dziurzanski, Shuai Zhao, Jerry Swan, Leandro Soares Indrusiak, Sebastian Scholze, and Karl Krone
• 4. Quantifying the effects of increasing user choice in MAP-elites applied to a workforce scheduling and routing problem / Neil Urquhart, Emma Hart, and William Hutcheson
• 5. A hybrid multi-objective differential evolution approach to stator winding optimization / André M. Silva, Fernando J.T.E. Ferreira, and Carlos Henggeler Antunes
• 6. GA-Novo : de novo peptide sequencing via tandem mass spectrometry using genetic algorithm / Samaneh Azari, Bing Xue, Mengjie Zhang, and Lifeng Peng
• 7. Ant colony optimization for optimized operation scheduling of combined heat and power plants / Johannes Mast, Stefan Rädle, Joachim Gerlach, and Oliver Bringmann
• 8. A flexible dissimilarity measure for active and passive 3D structures and its application in the fitness-distance analysis / Maciej Komosinski and Agnieszka Mensfelt.

• [II]. Games: 9. Free form evolution for Angry Birds level generation / Laura Calle, Juan J. Merelo, Antonio Mora-García, and José-Mario García-Valdez
• 10. Efficient online hyperparameter adaptation for deep reinforcement learning / Yinda Zhou, Weiming Liu, and Bin Li
• 11. GAMER : a genetic algorithm with motion encoding reuse for action-adventure video games / Tasos Papagiannis, Georgios Alexandridis, and Andreas Stafylopatis
• 12. Effects of input addition in learning for adaptive games : towards learning with structural changes / Iago Bonnici, Abdelkader Gouaïch, and Fabien Michel.

• [III]. General: 13. Supporting medical decisions for treating rare diseases through genetic programming / Illya Bakurov, Mauro Castelli, Leonardo Vanneschi, and Maria João Freitas
• 14. Evolutionary successful strategies in a transparent iterated prisoner's dilemma / Anton M. Unakafov, Thomas Schultze, Igor Kagan, Sebastian Moeller, Alexander Gail, Stefan Treue, Stephan Eule, and Fred Wolf
• 15. Evolutionary algorithms for the design of quantum protocols / Walter Krawec, Stjepan Picek, and Domagoj Jakobovic
• 16. Evolutionary computation techniques for constructing SAT-based attacks in algebraic cryptanalysis / Artem Pavlenko, Alexander Semenov, and Vladimir Ulyantsev
• 17. On the use of evolutionary computation for in-silico medicine : modelling sepsis via evolving continuous petri nets / Ahmed Hallawa, Elisabeth Zechendorf, Yi Song, Anke Schmeink, Arne Peine, Lukas Marin, Gerd Ascheid, and Guido Dartmann
• 18. A cultural algorithm for determining similarity values between users in recommender systems / Kalyani Selvarajah, Ziad Kobti, and Mehdi Kargar.

• [IV]. Image and signal processing: 19. Optimizing the C index using a canonical genetic algorithm / Thomas A. Runkler and James C. Bezdek
• 20. Memetic evolution of classification ensembles / Szymon Piechaczek, Michal Kawulok, and Jakub Nalepa
• 21. Genetic programming for feature selection and feature combination in salient object detection / Shima Afzali, Harith Al-Sahaf, Bing Xue, Christopher Hollitt, and Mengjie Zhang
• 22. Variable-length representation for EC-based feature selection in high-dimensional data / N.D. Cilia, C. De Stefano, F. Fontanella, and A. Scotto di Freca.

• [V]. Life sciences: 23. A knowledge based differential evolution algorithm for protein structure prediction / Pedro H. Narloch and Márcio Dorn
• 24. A biased random key genetic algorithm with local search chains for molecular docking / Pablo F. Leonhart and Márcio Dorn
• 25. Self-sustainability challenges of plants colonization strategies in virtual 3D environments / Kevin Godin-Dubois, Sylvain Cussat-Blanc, and Yves Duthen.

• [VI]. Networks and distributed systems: 26. Early detection of Botnet activities using grammatical evolution / Selim Yilmaz and Sevil Sen
• 27. Exploring concurrent and stateless evolutionary algorithms / Juan J. Merelo, J.L.J. Laredo, Pedro A. Castillo, José-Mario García-Valdez, and Sergio Rojas-Galeano
• 28. Evolving trust formula to evaluate data trustworthiness in VANETs using genetic programming / Mehmet Aslan and Sevil Sen
• 29. A matheuristic for green and robust 5G virtual network function placement / Thomas Bauschert, Fabio D'Andreagiovanni, Andreas Kassler, and Chenghao Wang
• 30. Prolong the network lifetime of wireless underground sensor networks by optimal relay node placement / Nguyen Thi Tam, Huynh Thi Thanh Binh, Tran Huy Hung, Dinh Anh Dung, and Le Trong Vinh.

• [VII]. Neuroevolution and data analytics: 31. The evolution of self-taught neural networks in a multi-agent environment / Nam Le, Anthony Brabazon, and Michael O'Neill
• 32. Coevolution of generative adversarial networks / Victor Costa, Nuno Lourenço, and Penousal Machado
• 33. Evolving recurrent neural networks for time series data prediction of coal plant parameters / AbdElRahman ElSaid, Steven Benson, Shuchita Patwardhan, David Stadem, and Travis Desell
• 34. Improving NeuroEvolution efficiency by surrogate model-based optimization with phenotypic distance kernels / Jörg Stork, Martin Zaefferer, and Thomas Bartz-Beielstein.

• [VIII]. Numerical optimization : theory, benchmarks and applications: 35. Compact optimization algorithms with re-sampled inheritance / Giovanni Iacca and Fabio Caraffini
• 36. Particle swarm optimization : understanding order-2 stability guarantees / Christopher W. Cleghorn
• 37. Fundamental flowers : evolutionary discovery of coresets for classification / Pietro Barbiero and Alberto Tonda.

This book constitutes the refereed proceedings of the 22nd International Conference on Applications of Evolutionary Computation, EvoApplications 2019, held in Leipzig, Germany, in April 2019, co-located with the Evo*2019 events EuroGP, EvoCOP and EvoMUSART. The 44 revised full papers presented were carefully reviewed and selected from 66 submissions. They were organized in topical sections named: Engineering and Real World Applications; Games; General; Image and Signal Processing; Life Sciences; Networks and Distributed Systems; Neuroevolution and Data Analytics; Numerical Optimization: Theory, Benchmarks, and Applications; Robotics.
(source: Nielsen Book Data)

• Applications of Evolutionary Computation
• Analysis of Evolutionary Computation Methods: Theory, Empirics, and Real-World Applications
• Computational Intelligence for Sustainability
• Evolutionary Computation in Edge, Fog, and Cloud Computing
• Evolutionary Machine Learning
• Machine Learning and AI in Digital Healthcare and Personalized Medicine
• Resilient Bio-Inspired Algorithms
• Soft Computing applied to Games
• Surrogate-Assisted Evolutionary Optimisation.

This book constitutes the refereed proceedings of the 25th International Conference on Applications of Evolutionary Computation, EvoApplications 2023, held as part of Evo*2023, in April 2023, co-located with the Evo*2023 events EuroGP, EvoCOP, and EvoMUSART. The EuroGP focused on the technique of genetic programming, EvoCOP targeted evolutionary computation in combinatorial optimization, and EvoMUSART was dedicated to evolved and bio-inspired music, sound, art, and design. The EvoApplications 2023 presents papers on the different areas: Analysis of Evolutionary Computation Methods: Theory, Empirics, and Real-World Applications, Applications of Bio-inspired Techniques on Social Networks, Evolutionary Computation in Edge, Fog, and Cloud Computing, Evolutionary Computation in Image Analysis, Signal Processing, and Pattern Recognition and others.

• pt. 1. EvoApplications 2011 : EvoCOMPLEX, EvoGAMES, EvoIASP, EvoINTELLIGENCE, EvoNUM, and EvoSTOC, Torino, Italy, April 27-29, 2011, proceedings
• pt. 2. EvoApplications 2011 : EvoCOMNET, EvoFIN, EvoHOT, EvoMUSART, EvoSTIM, and EvoTRANSLOG, Torino, Italy, April 27-29, 2011, proceedings.

This book constitutes the refereed proceedings of the International Conference on the Applications of Evolutionary Computation, EvoApplications 2011, held in Torino, Italy, in April 2011 colocated with the Evo 2011 events. Thanks to the large number of submissions received, the proceedings for EvoApplications 2011 are divided across two volumes (LNCS 6624 and 6625). The present volume contains contributions for EvoComnet, EvoFin, EvoIhot, EvoMusart, EvoStim, and EvoTransloc. The 51 revised full papers presented were carefully reviewed and selected from numerous submissions. This volume presents an overview about the latest research in EC. Areas where evolutionary computation techniques have been applied range from telecommunication networks to complex systems, finance and economics, games, image analysis, evolutionary music and art, parameter optimization, scheduling, and logistics. These papers may provide guidelines to help new researchers tackling their own problem using EC.
(source: Nielsen Book Data)

• Basics.- Research in Evolutionary Computation.- The New Experimentalism.- Statistics for Computer Experiments.- Optimization Problems.- Designs for Computer Experiments.- Search Algorithms.- Results and Perspectives.- Comparison.- Understanding Performance.- Summary and Outlook.
• (source: Nielsen Book Data)

This book introduces the new experimentalism in evolutionary computation, providing tools to understand algorithms and programs and their interaction with optimization problems. It develops and applies statistical techniques to analyze and compare modern search heuristics such as evolutionary algorithms and particle swarm optimization. The book bridges the gap between theory and experiment by providing a self-contained experimental methodology and many examples.
(source: Nielsen Book Data)

• Chapter 1 Introduction.-
• Part 1 Basic Concepts.-
• Chapter 2 Multi-objective Optimization Problem.-
• Chapter 3 Treatment of multi-objective Optimization Problem.-
• Part 2 Methodology.-
• Chapter 4 Self-Adaptive Multi-objective Optimization Differential Evolution.-
• Part 3 Applications.-
• Chapter 5 Mathematical.-
• Chapter 6 Engineering.-
• Part 4 Final Considerations.-
• Chapter 7 Conclusions.
• (source: Nielsen Book Data)

This book is aimed at undergraduate and graduate students in applied mathematics or computer science, as a tool for solving real-world design problems. The present work covers fundamentals in multi-objective optimization and applications in mathematical and engineering system design using a new optimization strategy, namely the Self-Adaptive Multi-objective Optimization Differential Evolution (SA-MODE) algorithm. This strategy is proposed in order to reduce the number of evaluations of the objective function through dynamic update of canonical Differential Evolution parameters (population size, crossover probability and perturbation rate). The methodology is applied to solve mathematical functions considering test cases from the literature and various engineering systems design, such as cantilevered beam design, biochemical reactor, crystallization process, machine tool spindle design, rotary dryer design, among others.
(source: Nielsen Book Data)

• Wilson Lamb: Applying functional analytic techniques to evolution equations.- Adam Bobrowski: Boundary conditions in evolutionary equations in biology.-Ernesto Estrada: Introduction to Complex Networks: Structure and Dynamics.-Jacek Banasiak: Kinetic models in natural sciences.- Philippe Laurencot: Weak compactness techniques and coagulation equations.- Ryszard Rudnicki: Stochastic operators and semigroups and their applications in physics and biology.- Mustapha Mokhtar-Kharroubi: Spectral theory for neutron transport.-Anna Marciniak-Czochra: Reaction-diffusion-ODE models of pattern formation.- Mapundi Kondwani Banda: Nonlinear Hyperbolic Systems of Conservation Laws and Related Applications.
• (source: Nielsen Book Data)

With the unifying theme of abstract evolutionary equations, both linear and nonlinear, in a complex environment, the book presents a multidisciplinary blend of topics, spanning the fields of theoretical and applied functional analysis, partial differential equations, probability theory and numerical analysis applied to various models coming from theoretical physics, biology, engineering and complexity theory. Truly unique features of the book are: the first simultaneous presentation of two complementary approaches to fragmentation and coagulation problems, by weak compactness methods and by using semigroup techniques, comprehensive exposition of probabilistic methods of analysis of long term dynamics of dynamical systems, semigroup analysis of biological problems and cutting edge pattern formation theory. The book will appeal to postgraduate students and researchers specializing in applications of mathematics to problems arising in natural sciences and engineering.
(source: Nielsen Book Data)

• Introduction. Methods of Optimal Search and Screening. Methods of Optimal Foraging. Models of Individual Search and Foraging. Coalitional Search and Swarm Dynamics. Remarks on Swarm Robotic Systems for Search and Foraging. Conclusion. Bibliography. Index.
• (source: Nielsen Book Data)

• Introduction Group Testing Search and Screening Games of Search Foraging Goal and Structure of This Book
• Methods of Optimal Search and Screening Location Probabilities and Search Density Search for a Static Target Search for a Moving Target
• Methods of Optimal Foraging Preying and Foraging by Patches Spatial Dynamics of Populations Methods of Optimal Foraging Inferences and Restrictions
• Models of Individual Search and Foraging Movements of the Agents and Their Trajectories Brownian Search and Foraging Foraging by Levy Flights Algorithms of Probabilistic Search and Foraging
• Coalitional Search and Swarm Dynamics Swarming and Collective Foraging Foraging by Multiple Foragers in Random Environment Modeling by Active Brownian Motion Turing System for the Swarm Foraging
• Remarks on Swarm Robotic Systems for Search and Foraging
• Conclusion
• Bibliography
• Index
• A Summary appears at the end of each chapter.
• (source: Nielsen Book Data)

Since the start of modern computing, the studies of living organisms have inspired the progress in developing computers and intelligent machines. In particular, the methods of search and foraging are the benchmark problems for robotics and multi-agent systems. The highly developed theory of search and screening involves optimal search plans that ar.
(source: Nielsen Book Data)
Since the start of modern computing, the studies of living organisms have inspired the progress in developing computers and intelligent machines. In particular, the methods of search and foraging are the benchmark problems for robotics and multi-agent systems. The highly developed theory of search and screening involves optimal search plans that are obtained by standard optimization techniques while the foraging theory addresses search plans that mimic the behavior of living foragers. Search and Foraging: Individual Motion and Swarm Dynamics examines how to program artificial search agents so that they demonstrate the same behavior as predicted by the foraging theory for living organisms. For cybernetics, this approach yields techniques that enable the best online search planning in varying environments. For biology, it allows reasonable insights regarding the internal activity of living organisms performing foraging tasks. The book discusses foraging theory as well as search and screening theory in the same mathematical and algorithmic framework. It presents an overview of the main ideas and methods of foraging and search theories, making the concepts of one theory accessible to specialists of the other. The book covers Brownian walks and Levy flight models of individual foraging and corresponding diffusion models and algorithms of search and foraging in random environments both by single and multiple agents. It also describes the active Brownian motion models for swarm dynamics with corresponding Fokker-Planck equations. Numerical examples and laboratory verifications illustrate the application of both theories.
(source: Nielsen Book Data)

• chapter 1. Introduction
• chapter 2. Methods of optimal search and screening
• chapter 3. Methods of optimal foraging
• chapter 4. Models of individual search and foraging
• chapter 5. Coalitional search and swarm dynamics
• chapter 6. Remarks on swarm robotic systems for search and foraging
• chapter 7. Conclusion

• Introduction.- Fundamentals of Search, Optimization and Decision Making.- Multiobjective EA Basics.- Multiobjective Cybernetics - Does Nature Solve Problems and Are They Multiobjective?.- Modularity Causes Multiple Objectives in Natural and Computational Systems.- Problem Decomposition, Modularity and their relation to Multiple Objectives.- Spatial Predator-Prey Models of Multiobjective Optimization.- Solution Concepts in Co-evolution and Multi-objective Search.- How Multiple Objectives are Used and their Effects on Solution Selection Methods.- Ill-Defined Problem Spaces.- Constrained Optimization via MOEAs.- Multiobjectivization.- Helper Objectives.- Learning Evaluation Functions for Global Optimization.- Assessing the Intrinsic Number of Objectives.- Assessing the Intrinsic Number of Decision Variables.- Fuzzy Dominance, Favour and other Relations beyond Pareto Optimality.- Multiobjective Clustering.- Reducing Bloat in GP with Multiple Objectives.- Multiobjective GP for Human-Understandable Models.- Multiobjective Supervised Learning.- Multiobjective Association Rule Mining.- Protein-folding via MOEAs and Solution Selection.- Unveiling Salient Insights in Engineering Designs with MOEAs.- Conclusions.- MOEA: Triumph of Natural Computing.- References.- Index.
• (source: Nielsen Book Data)

Multiobjective problems involve several competing measures of solution quality, and multiobjective evolutionary algorithms (MOEAs) and multiobjective problem solving have become important topics of research in the evolutionary computation community over the past 10 years. This is an advanced text aimed at researchers and practitioners in the area of search and optimization. The book focuses on how MOEAs and related techniques can be used to solve problems, particularly in the disciplines of science and engineering. Contributions by leading researchers show how the concepts of multi objective optimization can be used to reformulate and resolve problems in broad areas such as constrained optimization, co evolution, classification, inverse modeling and design. The book is distinguished from other texts on MOEAs in that it is not primarily about the algorithms, nor specific applications, but about the concepts and processes involved in solving problems using a multi objective approach. Each chapter contributes to the central, deep concepts and themes of the book: evaluating the utility of the multi objective approach; discussing alternative problem formulations; showing how problem formulation affects the search process; and examining solution selection and decision making. The book will be of benefit to researchers, practitioners and graduate students engaged with optimization-based problem solving. For multi objective optimization experts, the book is an up-to-date account of emerging and advanced topics; for others, the book indicates how the multi objective approach can lead to fresh insights.
(source: Nielsen Book Data)

• Evolutionary Algorithms for Data Mining and Knowledge Discovery.- Strategies for Scaling Up Evolutionary Instance Reduction Algorithms for Data Mining.- GAP: Constructing and Selecting Features with Evolutionary Computing.- Multi-Agent Data Mining using Evolutionary Computing.- A Rule Extraction System with Class-Dependent Features.- Knowledge Discovery in Data Mining via an Evolutionary Algorithm.- Diversity and Neuro-Ensemble.- Unsupervised Niche Clustering: Discovering an Unknown Number of Clusters in Noisy Data Sets.- Evolutionary Computation in Intelligent Network Management.- Genetic Programming in Data Mining for Drug Discovery.- Microarray Data Mining with Evolutionary Computation.- An Evolutionary Modularized Data Mining Mechanism for Financial Distress Forecasts.
• (source: Nielsen Book Data)

Data mining (DM) consists of extracting interesting knowledge from re- world, large & complex data sets; and is the core step of a broader process, called the knowledge discovery from databases (KDD) process. In addition to the DM step, which actually extracts knowledge from data, the KDD process includes several preprocessing (or data preparation) and post-processing (or knowledge refinement) steps. The goal of data preprocessing methods is to transform the data to facilitate the application of a (or several) given DM algorithm(s), whereas the goal of knowledge refinement methods is to validate and refine discovered knowledge. Ideally, discovered knowledge should be not only accurate, but also comprehensible and interesting to the user. The total process is highly computation intensive. The idea of automatically discovering knowledge from databases is a very attractive and challenging task, both for academia and for industry. Hence, there has been a growing interest in data mining in several AI-related areas, including evolutionary algorithms (EAs). The main motivation for applying EAs to KDD tasks is that they are robust and adaptive search methods, which perform a global search in the space of candidate solutions (for instance, rules or another form of knowledge representation).
(source: Nielsen Book Data)

• 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.

• Parameter Setting in EAs: a 30 Year Perspective.- Parameter Control in Evolutionary Algorithms.- Self-Adaptation in Evolutionary Algorithms.- Adaptive Strategies for Operator Allocation.- Sequential Parameter Optimization Applied to Self-Adaptation for Binary-Coded Evolutionary Algorithms.- Combining Meta-EAs and Racing for Difficult EA Parameter Tuning Tasks.- Genetic Programming: Parametric Analysis of Structure Altering Mutation Techniques.- Parameter Sweeps for Exploring Parameter Spaces of Genetic and Evolutionary Algorithms.- Adaptive Population Sizing Schemes in Genetic Algorithms.- Population Sizing to Go: Online Adaptation Using Noise and Substructural Measurements.- Parameter-less Hierarchical Bayesian Optimization Algorithm.- Evolutionary Multi-Objective Optimization Without Additional Parameters.- Parameter Setting in Parallel Genetic Algorithms.- Parameter Control in Practice.- Parameter Adaptation for GP Forecasting Applications.
• (source: Nielsen Book Data)

One of the main difficulties of applying an evolutionary algorithm (or, as a matter of fact, any heuristic method) to a given problem is to decide on an appropriate set of parameter values. Typically these are specified before the algorithm is run and include population size, selection rate, operator probabilities, not to mention the representation and the operators themselves. This book gives the reader a solid perspective on the different approaches that have been proposed to automate control of these parameters as well as understanding their interactions. The book covers a broad area of evolutionary computation, including genetic algorithms, evolution strategies, genetic programming, estimation of distribution algorithms, and also discusses the issues of specific parameters used in parallel implementations, multi-objective evolutionary algorithms, and practical consideration for real-world applications. It is a recommended read for researchers and practitioners of evolutionary computation and heuristic methods.
(source: Nielsen Book Data)