%{search_type} search results

• Adaptation, Self-Adaptation and Parameter Tuning
• Classifier Systems, Differential Evolution and Swarm Intelligence
• Coevolution and Artificial Immune Systems
• Constraint Handling
• Dynamic and Uncertain Environments
• Estimation of Distribution Algorithms and Metamodelling
• Genetic Programming
• Multi-objective Optimisation
• Parallel Algorithms and Hardware Implementations
• Real-World Applications
• Theory.

This book constitutes the refereed proceedings of the 13th International Conference on Parallel Problem Solving from Nature, PPSN 2013, held in Ljubljana, Slovenia, in September 2014. The total of 90 revised full papers were carefully reviewed and selected from 217 submissions. The meeting began with 7 workshops which offered an ideal opportunity to explore specific topics in evolutionary computation, bio-inspired computing and metaheuristics. PPSN XIII also included 9 tutorials. The papers are organized in topical sections on adaption, self-adaption and parameter tuning; classifier system, differential evolution and swarm intelligence; coevolution and artificial immune systems; constraint handling; dynamic and uncertain environments; estimation of distribution algorithms and metamodelling; genetic programming; multi-objective optimisation; parallel algorithms and hardware implementations; real world applications; and theory.

• Computational brittleness and the evolution of computer viruses
• Evolutionary computing in multi-agent environments: Speciation and symbiogenesis
• Evolution strategies with subjective selection
• Emergent cooperation for multiple agents using genetic programming
• Evolution programs evolved
• Encoding scheme issues for open-ended artificial evolution
• Hardware evolution at function level
• Coevolutionary life-time learning
• Genetic programs and co-evolution
• Self-assemblage of gene nets in evolution via recruiting of new netters
• A survey of intron research in genetics
• Analytical and numerical investigations of evolutionary algorithms in continuous spaces
• On the asymptotic behavior of multirecombinant Evolution Strategies
• Are long path problems hard for genetic algorithms?
• Random tree generation for genetic programming
• Implicit formae in genetic algorithms
• A probabilistic database approach to the analysis of genetic algorithms
• Mean field analysis of tournament selection on a random manifold
• From recombination of genes to the estimation of distributions I. Binary parameters
• From recombination of genes to the estimation of distributions II. Continuous parameters
• Searching in the presence of noise
• The density of states
• A measure of the difficulty of optimisation problems
• On interactive evolutionary algorithms and stochastic mealy automata
• The influence of different coding schemes on the computational complexity of genetic algorithms in function optimization
• An analysis of the effects of neighborhood size and shape on local selection algorithms
• Evolutionary computation at the edge of feasibility
• Dimensional analysis of allele-wise mixing revisited
• Gaussian diffusion in a simple genetic algorithm
• Erroneous truncation selection
• A breeder's decision making perspective
• New crossover methods for sequencing problems
• The effect of extensive use of the mutation operator on generalization in genetic programming using sparse data sets
• On permutation representations for scheduling problems
• Multi-parent's niche: N-ary crossovers on NK-landscapes
• A preliminary investigation into directed mutations in evolutionary algorithms
• Heuristic crossovers for real-coded genetic algorithms based on fuzzy connectives
• Are evolutionary algorithms improved by large mutations?
• Mutation by imitation in boolean evolution strategies
• Formal algorithms + formal representations =search strategies
• A genetic algorithm with variable range of local search for tracking changing environments
• An Evolution Strategy with adaptation of the step sizes by a variance function
• Every niching method has its niche: Fitness sharing and implicit sharing compared
• Effects of isolation in a distributed population genetic algorithm
• Self-adaptive genetic algorithm for numeric functions
• Niche search: An evolutionary algorithm for global optimisation
• Adaptively parameterised evolutionary systems: Self adaptive recombination and mutation in a genetic algorithm
• Obtaining multiple distinct solutions with genetic algorithm niching methods
• Cost Based Operator Rate Adaptation: An investigation
• Genetic algorithms and relational landscapes
• IOGA: An instance-oriented genetic algorithm
• Explicit filtering of building blocks for genetic algorithms
• Multi-objective optimization by means of the thermodynamical genetic algorithm
• Adaptation to a changing environment by means of the thermodynamical genetic algorithm
• The development of a dual-agent strategy for efficient search across whole system engineering design hierarchies
• A parallel cellular genetic algorithm used in finite element simulation
• A robust solution searching scheme in genetic search
• Solving MasterMind using GAs and simulated annealing: A case of dynamic constraint optimization
• Evolving compact solutions in genetic programming: A case study
• Climbing up NP-hard hills
• On the performance assessment and comparison of stochastic multiobjective optimizers
• Paginating the generalized newspapers
• A comparison of simulated annealing and a heuristic method
• A comparison of optimization techniques for integrated manufacturing planning and scheduling
• A comparison of search techniques on a wing-box optimisation problem
• A comparative study of evolutionary algorithms for on-line parameter tracking
• Modeling urban growth by cellular automata
• Democratic optimization for discrete and continuous systems
• A study of some properties of Ant-Q
• Immunoid: An immunological approach to decentralized behavior arbitration of autonomous mobile robots
• Parallelizable evolutionary dynamics principles for solving the maximum clique problem
• Significance of locality and selection pressure in the grand deluge evolutionary algorithm
• Parallel computing with DNA: Toward the anti-universal machine
• Tackling the "curse of dimensionality" of radial basis functional neural networks using a genetic algorithm
• A Three-stage method for designing Genetic Fuzzy Systems by learning from examples
• Learning heuristics for OBDD minimization by Evolutionary Algorithms
• Improving the generalization performance of multi-layer-perceptrons with population-based incremental learning
• Robust GP in robot learning
• A pattern recognition system using evolvable hardware
• Topology design of feedforward neural networks by genetic algorithms
• An evolution strategy for on-line optimisation of dynamic objective functions
• Exploiting competing subpopulations for automatic generation of test sequences for digital circuits
• Constraint handling in evolutionary search: A case study of the frequency assignment
• An application of genetic algorithms and neural networks to scheduling power generating systems
• Evolutionary algorithms for the calculation of electron distributions in Si-MOSFETs
• Refueling of a nuclear power plant: Comparison of a naive and a specialized mutation operator
• Genetic algorithms applied to the physical design of VLSI circuits: A survey
• Stochastic methods for transistor size optimization of CMOS VLSI circuits
• An adaptive parallel Genetic Algorithm for VLSI-layout optimization
• Genetic algorithms for protocol validation
• Constraint handling for the fault coverage code generation problem: An inductive evolutionary approach
• New genetic local search operators for the traveling salesman problem
• An evolutionary approach to hardware/software partitioning
• Evolutionary Air Traffic Flow Management for large 3D-problems
• Genetic-based dynamic load balancing: Implementation and evaluation
• Production scheduling with genetic algorithms and simulation
• Network optimization using evolutionary strategies
• Co-evolving parallel random number generators
• Scheduling by genetic local search with multi-step crossover
• Finding the conformation of organic molecules with genetic algorithms
• Investigating a Parallel Breeder Genetic Algorithm on the inverse Aerodynamic design
• An evolutionary design for f-? lenses
• Optimization of heat exchanger networks by means of evolution strategies
• Industrial plant pipe-route optimisation with genetic algorithms
• An evolutionary algorithm for design optimization of microsystems
• A learning classifier system for three-dimensional shape optimization.

This book constitutes the refereed proceedings of the International Conference on Evolutionary Computation held jointly with the 4th Conference on Parallel Problem Solving from Nature, PPSN IV, in Berlin, Germany, in September 1996. The 103 revised papers presented in the volume were carefully selected from more than 160 submissions. The papers are organized in sections on basic concepts of evolutionary computation (EC), theoretical foundations of EC, modifications and extensions of evolutionary algorithms, comparison of methods, other metaphors, and applications of EC in a variety of areas like ML, NNs, engineering, CS, OR, and biology. The book has a comprehensive subject index.

• Preface. Committee and Sponsors. Evolutionary computation in crack problems
• W. Beluch. Investigation of evolutionary algorithm effectiveness in optimal synthesis of certain mechanisms
• K. Bialas-Hetltowski, et al. Minimum heat losses subjected to stiffness constraints: window frame optimization
• R.A. Bialecki, M. Krol. Evolutionary computation in inverse problems
• T. Burczynski, et al. Hang-glider wing design by genetic optimization
• S. D'Angelo, et al. An error function for optimum dimension synthesis of mechanisms using genetic algorithms
• I.F. de Bustos, et al. Evolutionary computation in thermoelastic problems
• A. Dlugosz. Management of evolutionary MAS for multiobjective optimisation
• G. Dobrowalski, M. Kisiel-Dorohinicki. PAMUC: a new method to handle with constraints and multiobjectivity in evolutionary algorithms
• R. Filomeno Coelho, et al. A comparative analysis of "controlled elitism" in the NGSA-II applied to frame optimization
• D. Greiner, et al. IS-PAES: multiobjective optimization with efficient constraint handling
• A. Hernandez Aguirre, et al. Optimization of aligned fiber laminate composites
• Z. Hu, et al. Genetic algorithm for damage assessment
• V.T. Johnson, et al. Estimation of parameters for a hydrodynamic transmission system mathematical model with the application of genetic algorithm
• A. Kesy, et al. Study of safety of high-rise buildings using evolutionary search
• S. Khajhpour, D.E. Grierson. Structural design using genetic algorithm
• E. Kita, et al. The topology optimization using evolutionary search
• G. Kokot, P. Orantek. Identification of CMM parametric errors by hierarchical genetic strategy
• J. Kolodziej, et al. Genetic algorithm for fatigue crack detection in Timoshenko beam
• M. Krawczuk, et al. Multicriteria design optimization of robot gripper mechanisms
• S. Krenich. Optimal design of multiple clutch brakes using a multistage evolutionary method
• S. Krenich, A. Osyczka. Distributed evolutionary algorithms in optimization of nonlinear solids
• W. Kus, T. Burczynski. Adaptive penalty strategies in genetic search for problems with inequality and equality constraints
• C.-Y. Lin, W.-H. Wu. On the identification of linear elastic mechanical behaviour of orthopedic materials using evolutionary algorithms
• M. Magalhaes Dourado, et al. Ranking pareto optimal solutions in genetic algorithm by using undifferentiation interval method
• J. Montusiewicz. The effectiveness of probabilistic algorithms in shape and topology discrete optimisation of 2-D composite structures
• A. Muc. Genetic algorithms in optimisation of resin hardening technological processes
• A. Muc, P. Saj. Hybrid evolutionary algorithms in optimization of structures under dynamical loads
• P. Orantek. Evolutionary optimization system (EOS) for design automation
• O. Osyczka, et al. Evolutionary method for a universal motor geometry optimization
• G. Papa, B. Korousic-Seljak. A review of the development and application of cluster oriented genetic algorithms
• I.C. Parmee. Genetic algorithm optimization of hole shapes in a perforated elastic plate over a range of loads
• S. Vigdergauz. An object oriented library for evolutionary programs with applications in partitioning of finite element meshes
• J. Zola, et al.
• (source: Nielsen Book Data)

This book contains papers presented at the IUTAM Symposium on Evolutionary Methods in Mechanics held in Cracow, Poland, September 24-27, 2002. Recently, Evolutionary algorithms and methods have become the most effective tools for solving specific kinds of problems in mechanics, especially in structural and multidisciplinary optimization. The book is devoted to both theoretical and practical developments of computational mechanics methods drawing their inspiration from nature with particular emphasis on evolutionary models of computation such as genetic algorithms, evolutionary strategies, classifier systems, evolutionary programming and other evolutionary computation techniques in mechanics. The special emphasis is put on evolutionary optimization in various fields of mechanics. The subject of evolutionary optimization has recently experienced a remarkable growth. New concepts, approaches and applications are being continually and exploited to provide efficient tools for solving a variety of optimization problems in mechanics. Several important topics are presented: evolutionary methods in shape and topology optimization, evolutionary methods in size and material optimization, evolutionary methods in multiobjective optimization, in inverse problems of engineering mechanics, in biomechanics, coupling of evolutionary methods and neural networks in mechanics, methods of artificial intelligence in mechanics based on evolutionary approaches and other unconventional applications in mechanics. Research workers, specialists and professionals who deal with methods of artificial intelligence, especially in the field of evolutionary computation, structural and multidisciplinary optimization would be interested in this book.
(source: Nielsen Book Data)

• Intro
• Preface
• Organization
• Contents
• Applications of Evolutionary Computation
• An Enhanced Opposition-Based Evolutionary Feature Selection Approach
• 1 Introduction
• 2 Moth Flame Optimization
• 2.1 Binary Moth Flame Optimization
• 2.2 Binary Moth Flame Optimization for Feature Selection
• 3 The Proposed Approach
• 3.1 Initialization Using Opposition-Based Method
• 3.2 Retiring Flame
• 4 Experimental Setup and Results
• 5 Conclusions
• References
• A Methodology for Determining Ion Channels from Membrane Potential Neuronal Recordings
• 1 Introduction

• 2 Conductance-Based Model Description
• 3 Defining a Benchmark with Known Types of Ion Channels
• 4 Methodology and Experimental Setup
• 5 Experimental Results
• 6 Conclusions
• A Mathematical Description of the Models
• B Experimental Setup and Parameter Ranges
• References
• Swarm Optimised Few-View Binary Tomography
• 1 Introduction
• 2 Binary Tomographic Reconstruction
• 3 Swarm Optimisation
• 4 Constrained Search in High Dimensions
• 5 Reconstructions
• 6 Results
• 7 Discussion
• 8 Conclusions
• References

• Comparing Basin Hopping with Differential Evolution and Particle Swarm Optimization
• 1 Introduction
• 2 The Metaheuristics Studied
• 2.1 Basin Hopping
• 2.2 Differential Evolution
• 2.3 Particle Swarm Optimization
• 3 The Benchmarking Environment
• 4 Experimental Setup
• 5 Experimental Results
• 6 Conclusions
• References
• Combining the Properties of Random Forest with Grammatical Evolution to Construct Ensemble Models
• 1 Introduction
• 2 Methodology
• 2.1 Structured Grammatical Evolution
• 2.2 Random Structured Grammatical Evolution for Symbolic Regression Problems

• 3 Experimental Setup
• 3.1 Study Problems
• 3.2 Configuration of the Algorithms
• 4 Results
• 5 Conclusions
• References
• EvoCC: An Open-Source Classification-Based Nature-Inspired Optimization Clustering Framework in Python
• 1 Introduction
• 2 Related Works
• 3 Methodology
• 4 Framework Overview
• 4.1 Parameters
• 4.2 Datasets
• 4.3 Clustering with EvoCluster
• 4.4 Classification
• 4.5 Evaluation Measures
• 4.6 Results Management
• 5 Experiments and Visualizations
• 6 Conclusion and Future Works
• References
• Evolution of Acoustic Logic Gates in Granular Metamaterials

• 1 Introduction
• 2 Problem Statement
• 3 Simulation Setup
• 3.1 2D Granular Simulator
• 3.2 Optimization Method
• 4 Results and Discussion
• 4.1 Evolution of an Acoustic Band Gap
• 4.2 Evolving an AND Gate
• 4.3 Evolving an XOR Gate
• 5 Conclusion and Future Work
• References
• Public-Private Partnership: Evolutionary Algorithms as a Solution to Information Asymmetry
• 1 Introduction
• 2 The Problem
• 3 Proposed Approach
• 3.1 The Model
• 3.2 Data
• 3.3 Adversarial Optimization
• 3.4 Operator (EA1)
• 3.5 Public Administration (EA2)
• 4 Experimental Evaluation

This book constitutes the refereed proceedings of the 25th International Conference on Applications of Evolutionary Computation, EvoApplications 2022, held as part of Evo*2022, in April 2022, co-located with the Evo*2022 events EuroGP, EvoCOP, and EvoMUSART. The 46 revised full papers presented in this book were carefully reviewed and selected from 67 submissions. .
(source: Nielsen Book Data)

• On Restricting Real-Valued Genotypes in Evolutionary Algorithms.- Towards Explainable Exploratory Landscape Analysis: Extreme Feature Selection for Classifying BBOB Functions.- Co-Optimising Robot Morphology and Controller in a Simulated Open-ended Environment.- Multi-objective workforce allocation in construction projects.- Generating Duplex Routes for Robust Bus Transport Network by Improved Multi-objective Evolutionary Algorithm based on Decomposition.- Combining Multi-objective Evolutionary Algorithms with deep generative models towards focused molecular design.- A Multi-Objective Evolutionary Algorithm Approach for Optimizing Part Quality Aware Assembly Job Shop Scheduling Problems.- Evolutionary Grain-Mixing to Improve Profitability in Farming Winter Wheat.- Automatic Modular Design of Behavior Trees for Robot Swarms with Communication Capabilites.- Salp Swarm Optimization Search Based Feature Selection for Enhanced Phishing Websites Detection.- Real Time Optimisation of Traffic Signals to Prioritise Public Transport.- Adaptive Covariance Pattern Search.- Evaluating the Success-History based Adaptive Differential Evolution in the Protein Structure Prediction problem.- Beyond Body Shape and Brain: Evolving the Sensory Apparatus of Voxel-based Soft Robots.- Desirable Objective Ranges in Preference-based Evolutionary Multiobjective Optimization.- Improving Search Efficiency and Diversity of Solutions in Multiobjective Binary Optimization by Using Metaheuristics plus Integer Linear Programming.- Automated, Explainable Rule Extraction from MAP-Elites archives.- EDM-DRL: Toward Stable Reinforcement Learning through Ensembled Directed Mutation.- Continuous Ant-Based Neural Topology.- Playing with Dynamic Systems - Battling Swarms in Virtual Reality.- EvoCraft: A New Challenge for Open-Endedness.- A Profile-Based 'GrEvolutionary' Hearthstone Agent.- Modelling Asthma Patients' Responsiveness to Treatment Using Feature Selection and Evolutionary Computation.- Bayesian Networks for Mood Prediction Using Unobtrusive Ecological Momentary Assessments.- A Multi-Objective Multi-Type Facility Location Problem for the Delivery of Personalised Medicine.- RDE-OP: A Region-Based Differential Evolution Algorithm Incorporation Opposition-Based Learning for Optimising the Learning Process of Multi-Layer Neural Networks.- Estimation of Grain-level Residual Stresses in a Quenched Cylindrical Sample of Aluminum Alloy AA5083 using Genetic Programming.- EDA-based optimization of blow-off valve positions for centrifugal compressor systems.- 3D-2D Registration using X-ray Simulation and CMA-ES.- Lateralized Approach for Robustness AgainstAttacks in Emotion Categorization from Images.- Improved Crowding Distance in Multi-objective Optimization for Feature Selection in Classification.- Deep Optimisation: Multi-Scale Evolution by Inducing and Searching in Deep Representations.- Evolutionary Planning in Latent Space.- Utilizing the Untapped Potential of Indirect Encoding for Neural Networks with Meta Learning.- Effective Universal Unrestricted Adversarial Attacks using a MOE Approach.- Improving Distributed Neuroevolution Using Island Extinction and Repopulation.- An Experimental Study of Weight Initialization and Lamarckian Inheritance on Neuroevolution.- Towards Feature-Based Performance Regression Using Trajectory Data.- Demonstrating the Evolution of GANs through t-SNE.- Optimising diversity in classifier ensembles of classification trees.- WILDA: Wide Learning of Diverse Architectures for Classification of Large Datasets.- Evolving Character-Level DenseNet Architectures using Genetic Programming.- Transfer Learning for Automated Test Case Prioritization using XCSF.- On the Effects of Absumption for XCS with Continuous-Valued Inputs.- A NEAT Visualisation of Neuroevolution Trajectories.- Evaluating Models with Dynamic Sampling Holdout.- Event-driven multi-algorithm optimization: mixing Swarm and Evolutionary strategies.- TensorGP - Genetic Programming Engine in TensorFlow.- A novel evolutionary approach for IoT-based water contaminant detection.- Evolutionary Algorithms for Roughness Coefficient Estimation in River Flow Analyses.- EA-based ASV Trajectory Planner for Pollution Detection in Lentic Waters.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 24th International Conference on Applications of Evolutionary Computation, EvoApplications 2021, held as part of Evo*2021, as Virtual Event, in April 2021, co-located with the Evo*2021 events EuroGP, EvoCOP, and EvoMUSART. The 51 revised full papers presented in this book were carefully reviewed and selected from 78 submissions. The papers cover a wide spectrum of topics, ranging from applications of evolutionary computation; applications of deep bioinspired algorithms; soft computing applied to games; machine learning and AI in digital healthcare and personalized medicine; evolutionary computation in image analysis, signal processing and pattern recognition; evolutionary machine learning; parallel and distributed systems; and applications of nature inspired computing for sustainability and development. .
(source: Nielsen Book Data)

• Applications of Evolutionary Computation.- A Local Search for Numerical Optimisation based on Covariance Matrix Diagonalisation.- EvoCluster: An Open-Source Nature-Inspired Optimization Clustering Framework in Python.- Optimizing the Hyperparameters of a Mixed Integer Linear Programming Solver to Speed Up Electric Vehicle Charging Control.- Automatic rule extraction from access rules using Genetic Programming.- Search Trajectory Networks of Population-based Algorithms in Continuous Spaces.- Evolving-controllers versus learning-controllers for morphologically evolvable robots.- Simulation-driven multi-objective evolution for traffic light optimization.- Automatic Generation of Adversarial Metamorphic Malware Using MAP-Elites.- EvoDynamic: a framework for the evolution of generally represented dynamical systems and its application to criticality.- A Decomposition-Based Evolutionary Algorithm with Adaptive Weight Vectors for Multi- and Many-objective Optimization.- Differential Evolution Multi-Objective for Tertiary Protein Structure Prediction.- Particle Swarm Optimization: A Wrapper-based Feature Selection Method for Ransomware Detection and Classification.- A method for estimating the computational complexity of multimodal functions.- Locating Odour Sources with Geometric Syntactic Genetic Programming.- Designing cable-stayed bridges with Genetic Algorithms.- A fast, scalable meta-heuristic for network slicing under traffic uncertainty.- What is Your MOVE: Modeling Adversarial Network Environments.- Using evolution to design modular robots: An empirical approach to select module designs.- Iterated Granular Neighborhood Algorithm for the Taxi Sharing Problem.- Applications of Bio-inspired techniques on Social Networks.- Multiobjective Optimization of a Targeted Vaccination Scheme in the Presence of Non-diagnosed Cases.- Community Detection in Attributed Graphs with Differential Evolution.- Applications of Deep Bioinspired Algorithms.- Fake news detection using time series and user features classification.- Social Learning vs Self-teaching in a Multi-agent Neural Network System.- Evolving Instinctive Behaviour in Resource-Constrained Autonomous Agents Using Grammatical Evolution.- An Adversarial Optimization Approach for the Development of Robust Controllers.- Soft Computing Applied to Games.- Efficient Heuristic Policy Optimisation for a Challenging Strategic Card Game.- Finding Behavioural Patterns Among League of Legends Players Through Hidden Markov Models.- Learning the Designer's Preferences to Drive Evolution.- Testing hybrid computational intelligence algorithms for general game playing.- Evolutionary Computation in Digital Healthcare and Personalized Medicine.- Accelerated Design of HIFU Treatment Plans Using Island-based Evolutionary Strategy.- Using Genetic Algorithms for the prediction of cognitive impairments.- Short and Medium Term Blood Glucose Prediction using Multi-Objective Grammatical Evolution.- Evolutionary Machine Learning.- A Greedy Iterative Layered Framework for Training Feed Forward Neural Networks.- Evolution of Scikit-Learn Pipelines with Dynamic Structured Grammatical Evolution.- An Empirical Exploration of Deep Recurrent Connections Using Neuro-Evolution.- Using Skill Rating as Fitness on the Evolution of GANs.- A Local Search with a Surrogate Assisted Option for Instance Reduction.- Evolutionary Latent Space Exploration of Generative Adversarial Networks.- Neuro-Evolutionary Transfer Learning through Structural Adaptation.- Ant-based Neural Topology Search (ANTS) for Optimizing Recurrent Networks.- Parallel and Distributed Systems.- A MIMD interpreter for Genetic Programming.- Security Risk Optimization for Multi-Cloud Applications.- Using evolutionary algorithms for server hardening via the moving target defense technique.- An Event-based Architecture for Cross-Breed Multi-population Bio-inspired Optimization Algorithms.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 23rd European Conference on Applications of Evolutionary Computation, EvoApplications 2020, held as part of Evo*2020, in Seville, Spain, in April 2020, co-located with the Evo*2020 events EuroGP, EvoMUSART and EvoCOP. The 44 full papers presented in this book were carefully reviewed and selected from 62 submissions. The papers cover a wide spectrum of topics, ranging from applications of bio-inspired techniques on social networks, evolutionary computation in digital healthcare and personalized medicine, soft-computing applied to games, applications of deep-bioinspired algorithms, parallel and distributed systems, and evolutionary machine learning. .
(source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 11th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2011, held in Torino, Italy, in April 2011. The 22 revised full papers presented were carefully reviewed and selected from 42 submissions. The papers present the latest research and discuss current developments and applications in metaheuristics - a paradigm to effectively solve difficult combinatorial optimization problems appearing in various industrial, economical, and scientific domains. Prominent examples of metaheuristics are evolutionary algorithms, simulated annealing, tabu search, scatter search, memetic algorithms, variable neighborhood search, iterated local search, greedy randomized adaptive search procedures, estimation of distribution algorithms, and ant colony optimization.
(source: Nielsen Book Data)

• Machine generated contents note:
• Automated Innovization for Simultaneous Discovery of Multiple Rules in Bi-objective Problems
• Taxonomy of Online Stopping Criteria for Multi-Objective Evolutionary Algorithms / Sunith Bandaru / Kalyanmoy Deb
• Not All Parents Are Equal for MO-CMA-ES / Tobias Wagner / Heike Trautmann / Luis Marti
• On Sequential Online Archiving of Objective Vectors / Ilya Loshchilov / Marc Schoenauer / Michèle Sebag
• On a Stochastic Differential Equation Approach for Multiobjective Optimization up to Pareto-Criticality / Manuel López-Ibáñez / Joshua Knowles / Marco Laumanns
• Pareto Cone &Dominance: Improving Convergence and Diversity in Multiobjective Evolutionary Algorithms / Ricardo H.C. Takahashi / Eduardo G. Carrano / Elizabeth F. Wanner
• Variable Preference Modeling Using Multi-Objective Evolutionary Algorithms / Lucas S. Batista / Felipe Campelo / Frederico G. Guimarães /

• Note continued:
• Framework for Many-Objective Test Problems with Both Simple and Complicated Pareto-Set Shapes / Joseph M. Pasia / Hernán Aguirre / Kiyoshi Tanaka
• Preference Based Interactive Evolutionary Algorithm for Multi-objective Optimization: PIE / Dhish Kumar Saxena / Qingfu Zhang / João A. Duro / Ashutosh Tiwari
• Preference Ranking Schemes in Multi-Objective Evolutionary Algorithms / Karthik Sindhya / Ana Belen Ruiz / Kaisa Miettinen
• Interactive Multiobjective Mixed-Integer Optimization Using Dominance-Based Rough Set Approach / Marlon Alexander Braun / Pradyumn Kumar Shukla / Hartmut Schmeck
• Very Large-Scale Neighborhood Search for Solving Multiobjective Combinatorial Optimization Problems / Salvatore Greco / Benedetto Matainzzo / Roman Slowinski
• Bilevel Multi-objective Optimization Problem Solving Using Progressively Interactive EMO / Thibaut Lust / Jacques Teghem / Daniel Tuyttens
• Multi-objective Phylogenetic Algorithm: Solving Multi-objective Decomposable Deceptive Problems / Ankur Sinha
• Multi-objective Optimization with Joint Probabilistic Modeling of Objectives and Variables / Jean Paulo Martins / Antonio Helson Mineiro Soares / Danilo Vasconcellos Vargas / Alexandre Cláudio Botazzo Delbem
• Bi-objective Based Hybrid Evolutionary-Classical Algorithm for Handling Equality Constraints / Hossein Karshenas / Roberto Santana / Concha Bielza / Pedro Larrañaga
• New Memory Based Variable-Length Encoding Genetic Algorithm for Multiobjective Optimization / Rituparna Datta / Kalyanmoy Deb
• Concentration-Based Artificial Immune Network for Multi-objective Optimization / Eduardo G. Carrano / Livia A. Moreira / Ricardo H.C. Takahashi
• Bi-objective Portfolio Optimization Using a Customized Hybrid NSGA-II Procedure / Guilherme Palermo Coelho / Fernando J. Von Zuben

• Note continued:
• Aesthetic Design Using Multi-Objective Evolutionary Algorithms / Rahul Tewari / Rajat Tewari / Ralph E. Steuer / Kalyanmoy Deb
• Introducing Reference Point Using g-Dominance in Optimum Design Consideripg Uncertainties: An Application in Structural Engineering / Ferrie van Hattum / Dirk Loyens / António Gaspar-Cunha
• Multiobjective Dynamic Optimization of Vaccination Campaigns Using Convex Quadratic Approximation Local Search / José M. Emperador / Máximo Méndez / Gabriel Winter / David Greiner / Blas Galván
• Adaptive Technique to Solve Multi-objective Feeder Reconfiguration Problem in Real Time Context / Ricardo H.C. Takahashi / Rodrigo T.N. Cardoso / André R. da Cruz
• Variable Neighborhood Multiobjective Genetic Algorithm for the Optimization of Routes on IP Networks / Joao Antonio de Vasconcelos / Walmir Matos Caminhas / Carlos Henrique N. de Resende Barbosa
• Real-Time Estimation of Optical Flow Based on Optimized Haar Wavelet Features / Renata E. Onety / Ricardo H.C. Takahashi / Oriane M. Neto / Gladston J.P. Moreira
• Multi-objective Genetic Algorithm Evaluation in Feature Selection / Jan Salmen / Lukas Caup / Christian Igel
• Cultural Algorithm Applied in a Bi-Objective Uncapacitated Facility Location Problem / Ana Carolina Lorena / Huei Diana Lee / Newton Spolaôr
• Bi-objective Iterated Local Search Heuristic with Path-Relinking for the p-Median Problem / Ricardo Soto / Claudio Cubillos / Guillermo Cabrera / Boris Fernández / Daniela Diaz / José Miguel Rubio
• Framework for Locating Logistic Facilities with Multi-Criteria Decision Analysis / Wellington G. Ribeiro / André G. Santos / Paula M. dos Santos / José E.C. Arroyo
• Lorenz versus Pareto Dominance in a Single Machine Scheduling Problem with Rejection / Gilberto Montibeller / Hugo Yoshizaki

• Note continued:
• GRACE: A Generational Randomized ACO for the Multi-objective Shortest Path Problem / Lionel Amodeo / Farouk Yalaoui / Atefeh Moghaddam
• Modeling Decision-Maker Preferences through Utility Function Level Sets / Marco C. Goldbaiq / Luciana S. Buriol / Leonardo C.T. Bezerra / Elizabeth F.G. Goldbarg
• MCDM Model for Urban Water Conservation Strategies Adapting Simos Procedure for Evaluating Alternatives Intra-criteria / Luciana R. Pedro / Ricardo H.C. Takahashi
• Multicriteria Decision Model for a Combined Burn-In and Replacement Policy / Marcele Elisa Fontana / Danielle Costa Morais / Adiel Teixeira de Almeida
• Applying a Multicriteria Decision Model So as to Analyse the Consequences of Failures Observed in RCM Methodology / Cristiano Alexandre Virginio Cavalcante
• Supplier Selection Based on the PROMETHEE VI Multicriteria Method / Marcelo Hazin Alencar / Adiel Teixeira Almeida.

This book constitutes the refereed proceedings of the 6th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2011, held in Ouro Preto, Brazil, in April 2011. The 42 revised full papers presented were carefully reviewed and selected from 83 submissions. The papers deal with fundamental questions of EMO theory, such as the development of algorithmically efficient tools for the evaluation of solution-set quality, the theoretical questions related to solution archiving and others. They report on the continuing effort in the development of algorithms, either for dealing with particular classes of problems or for new forms of processing the problem information. Almost one third of the papers is related to EMO applications in a diversity of fields. Eleven papers are devoted to promote the interaction with the related field of Multi-Criterion Decision Making (MCDM).
(source: Nielsen Book Data)

• Set Oriented Numerics
• Computational Game Theory
• Machine Learning Applied to Networks
• Complex Networks and Landscape Analysis
• Local Search and Optimization
• Genetic Programming
• Evolutionary Multiobjective Optimization
• Practical Aspects of Evolutionary Algorithms.

This volume encloses research articles that were presented at the EVOLVE 2014 International Conference in Beijing, China, July 1-4, 2014. The book gathers contributions that emerged from the conference tracks, ranging from probability to set oriented numerics and evolutionary computation; all complemented by the bridging purpose of the conference, e.g. Complex Networks and Landscape Analysis, or by the more application oriented perspective. The novelty of the volume, when considering the EVOLVE series, comes from targeting also the practitioner's view. This is supported by the Machine Learning Applied to Networks and Practical Aspects of Evolutionary Algorithms tracks, providing surveys on new application areas, as in the networking area and useful insights in the development of evolutionary techniques, from a practitioner's perspective. Complementary to these directions, the conference tracks supporting the volume, follow on the individual advancements of the subareas constituting the scope of the conference, through the Computational Game Theory, Local Search and Optimization, Genetic Programming, Evolutionary Multi-objective optimization tracks.

• EvoCOMNET contributions
• EvoFIN contributions
• EvoIASP contributions -- EvoINTERACTION contributions
• EvoMUSART contributions
• EvoSTOC contributions
• EvoTRANSLOG contributions.

This book constitutes the refereed joint proceedings of seven workshops on evolutionary computing, EvoWorkshops 2007, held in Valencia, Spain in April 2007. It examines evolutionary computation in communications, networks, and connected systems; finance and economics; image analysis and signal processing; and transportation and logistics. Coverage also details evolutionary algorithms in stochastic and dynamic environments.
(source: Nielsen Book Data)

• 1. A cooperative optimization approach for distributing service points in mobility applications / Thomas Jatschka, Tobias Rodemann, and Günther R. Raidl
• 2. A binary algebraic differential evolution for the multidimensional two-way number partitioning problem / Valentino Santucci, Marco Baioletti, Gabriele Di Bari, and Alfredo Milani
• 3. A new representation in genetic programming for evolving dispatching rules for dynamic flexible job shop scheduling / Fangfang Zhang, Yi Mei, and Mengjie Zhang
• 4. An iterated local search algorithm for the two-machine flow shop problem with buffers and constant processing times on one machine / Hoang Thanh Le, Philine Geser, and Martin Middendorf
• 5. Route planning for a fleet of electric vehicles with waiting times at charging stations / Baoxiang Li, Shashi Shekhar Jha, and Hoong Chuin Lau
• 6. Multiple periods vehicle routing problems : a case study / Bilal Messaoudi, Ammar Oulamara, and Nastaran Rahmani
• 7. Rigorous performance analysis of state-of-the-art TSP heuristic solvers / Paul McMenemy, Nadarajen Veerapen, Jason Adair, and Gabriela Ochoa
• 8. Runtime analysis of discrete particle swarm optimization applied to shortest paths computation / Alexander Raß, Jonas Schreiner, and Rolf Wanka
• 9. Quasi-optimal recombination operator / Francisco Chicano, Gabriela Ochoa, Darrell Whitley, and Renato Tinós
• 10. Insights into the feature selection problem using local Optima networks / Werner Mostert, Katherine M. Malan, Gabriela Ochoa, and Andries P. Engelbrecht

• 11. Clarifying the difference in local Optima network sampling algorithms / Sarah L. Thomson, Gabriela Ochoa, and Sébastien Verel
• 12. A unifying view on recombination spaces and abstract convex evolutionary search / Marcos Diez García and Alberto Moraglio
• 13. Program trace optimization with constructive heuristics for combinatorial problems / James McDermott and Alberto Moraglio
• C1. Correction to: Multiple periods vehicle routing problems : a case study / Bilal Messaoudi, Ammar Oulamara, and Nastaran Rahmani.

This book constitutes the refereed proceedings of the 19th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2019, held as part of Evo* 2019, in Leipzig, Germany, in April 2019, co-located with the Evo* 2019 events EuroGP, EvoMUSART and EvoApplications. The 14 revised full papers presented were carefully reviewed and selected from 37 submissions. The papers cover a wide spectrum of topics, ranging from the foundations of evolutionary computation algorithms and other search heuristics to their accurate design and application to both single- and multi-objective combinatorial optimization problems. Fundamental and methodological aspects deal with runtime analysis, the structural properties of fitness landscapes, the study of metaheuristics core components, the clever design of their search principles, and their careful selection and configuration. Applications cover domains such as scheduling, routing, partitioning and general graph problems.
(source: Nielsen Book Data)

• NEW DEVELOPMENTS IN EVOLUTIONARY COMPUTATION RESEARCH; NEW DEVELOPMENTS IN EVOLUTIONARY COMPUTATION RESEARCH; LIBRARY OF CONGRESS CATALOGING-IN-PUBLICATION DATA; CONTENTS; PREFACE;
• Chapter 1: MULTI-OBJECTIVE OPTIMIZATIONOF TRADING STRATEGIES USING GENETICALGORITHMS IN UNSTABLE ENVIRONMENTS; Abstract; A. Part A. Review of the Main Problem Solving and Optimization Techniques; B. Part B.A Review of Main Multi-Objective Optimization Techniques; C. Part C.A Case Study; Conclusion; References.

• Chapter 2: PROMOTING BETTER GENERALISATION IN MULTI-LAYER PERCEPTRONS USING A SIMULATED SYNAPTIC DOWNSCALING MECHANISMAbstract; 1. Introduction; 2. Background; 3. Model and Experiments; 4. Results and Analysis; 5. Conclusion; Acknowledgment; References;
• Chapter 3: PLANT PROPAGATION-INSPIRED ALGORITHMS; Abstract; 1. Introduction; 2. Background; 3. Plant Propagation Algorithms; 4. Applications; 5. Conclusion; References;
• Chapter 4: TOPOGRAPHICAL CLEARING DIFFERENTIAL EVOLUTION APPLIED TO REAL-WORLD MULTIMODAL OPTIMIZATION PROBLEMS; Abstract; 1. Introduction.

• 2. The Differential Evolution Algorithm3. Topographical Clearing; 4. Numerical Comparisons; 5. Conclusion; Appendix A. Nonlinear Systems Formulated as Optimization Problems; Appendix B. Data and Fitted Variables for the Catalytic Reactor Model; References;
• Chapter 5: ROBOTICS, EVOLUTION AND INTERACTIVITY IN SONIC ART INSTALLATIONS; Abstract; Introduction; 1. JaVOX, an Evolutionary Composition System; 2. Generative Sonification; 3. Automation x Interactivity; 4. Interactivity, Evolution and Structure; Conclusion; Acknowledgments; References.

• Chapter
• 6: AN ANALYSIS OF EVOLUTIONARY-BASED SAMPLING METHODOLOGIESAbstract;
• 1. Introduction;
• 2. Background;
• 3. Numerical Experiments: Design and Implementation;
• 4. Results and Discussion;
• 5. Conclusion; References; Blank Page; INDEX.

A common approach for solving simulation-driven engineering problems is by using metamodel-assisted optimization algorithms, namely, in which a metamodel approximates the computationally expensive simulation and provides predicted values at a lower computational cost. Such algorithms typically generate an initial sample of solutions which are then used to train a preliminary metamodel and to initiate an optimization process. One approach for generating the initial sample is with the design of experiment methods which are statistically oriented, while the more recent search-driven sampling approach invokes a computational intelligence optimizer such as an evolutionary algorithm, and then uses the vectors it generated as the initial sample. This book discusses research and new developments on evolutionary computation.
(source: Nielsen Book Data)

• Fundamentals. Evolutionary Dynamic Optimization: Test and Evaluation Environments / Shengxiang Yang, Trung Thanh Nguyen, Changhe Li
• Evolutionary Dynamic Optimization: Methodologies / Trung Thanh Nguyen [and others]
• Evolutionary Dynamic Optimization: Challenges and Perspectives / Philipp Rohlfshagen, Xin Yao
• Dynamic Multi-objective Optimization: A Survey of the State-of-the-Art / Carlo Raquel, Xin Yao
• Algorithm Design. A Comparative Study on Particle Swarm Optimization in Dynamic Environments / Changhe Li, Shengxiang Yang
• Memetic Algorithms for Dynamic Optimization Problems / Hongfeng Wang, Shengxiang Yang
• BIPOP: A New Algorithm with Explicit Exploration/Exploitation Control for Dynamic Optimization Problems / Enrique Alba [and others]
• Evolutionary Optimization on Continuous Dynamic Constrained Problems -- An Analysis / Trung Thanh Nguyen, Xin Yao
• Theoretical Analysis. Theoretical Advances in Evolutionary Dynamic Optimization / Philipp Rohlfshagen, Per Kristian Lehre, Xin Yao
• Analyzing Evolutionary Algorithms for Dynamic Optimization Problems Based on the Dynamical Systems Approach / Renato Tinós, Shengxiang Yang
• Dynamic Fitness Landscape Analysis / Hendrik Richter
• Dynamics in the Multi-objective Subset Sum: Analysing the Behavior of Population Based Algorithms / Iulia Maria Comsa, Crina Grosan, Shengxiang Yang
• Applications. Ant Colony Optimization Algorithms with Immigrants Schemes for the Dynamic Travelling Salesman Problem / Michalis Mavrovouniotis, Shengxiang Yang
• Genetic Algorithms for Dynamic Routing Problems in Mobile Ad Hoc Networks / Hui Cheng, Shengxiang Yang
• Evolutionary Computation for Dynamic Capacitated Arc Routing Problem / Yi Mei, Ke Tang, Xin Yao
• Evolutionary Algorithms for the Multiple Unmanned Aerial Combat Vehicles Anti-ground Attack Problem in Dynamic Environments / Xingguang Peng [and others]
• Advanced Planning in Vertically Integrated Wine Supply Chains / Maksud Ibrahimov [and others].

This book provides a compilation on the state-of-the-art and recent advances of evolutionary computation for dynamic optimization problems. The motivation for this book arises from the fact that many real-world optimization problems and engineering systems are subject to dynamic environments, where changes occur over time. Key issues for addressing dynamic optimization problems in evolutionary computation, including fundamentals, algorithm design, theoretical analysis, and real-world applications, are presented. "Evolutionary Computation for Dynamic Optimization Problems" is a valuable reference to scientists, researchers, professionals and students in the field of engineering and science, particularly in the areas of computational intelligence, nature- and bio-inspired computing, and evolutionary computation

• Bio-inspired Optimization Methods
• Topics Organization
• Evolutionary Computation
• Evolutionary Computation Methods
• Properties of Genetic Algorithms
• Evolving Regression Models
• Identification
• Parameter Estimation
• Independent Component Analysis
• Time Series Linear and Nonlinear Models
• Models of Time Series
• Autoregressive Moving Average Models
• Nonlinear Models
• Design of Experiments
• Experiments and Design of Experiments.

• The Evolutionary Design of Experiments
• The Evolutionary Model-Based Experimental Design: The Statistical Models in the Evolution
• Outliers
• Outliers in Independent Data
• Outliers in Time Series
• Genetic Algorithms for Multiple Outlier Detection
• Cluster Analysis
• Partitioning Problem
• Genetic Clustering Algorithms
• Fuzzy Partition
• Multivariate Mixture Models Estimation by Evolutionary Computing
• Genetic Algorithms in Classification and Regression Trees Models
• Clusters of Time Series and Directional Data
• Multiobjective Genetic ClusteringReferences.

This proposed text appears to be a good introduction to evolutionary computation for use in applied statistics research. The authors draw from a vast base of knowledge about the current literature in both the design of evolutionary algorithms and statistical techniques. Modern statistical research is on the threshold of solving increasingly complex problems in high dimensions, and the generalization of its methodology to parameters whose estimators do not follow mathematically simple distributions is underway. Many of these challenges involve optimizing functions for which analytic solutions are infeasible. Evolutionary algorithms represent a powerful and easily understood means of approximating the optimum value in a variety of settings. The proposed text seeks to guide readers through the crucial issues of optimization problems in statistical settings and the implementation of tailored methods (including both stand-alone evolutionary algorithms and hybrid crosses of these procedures with standard statistical algorithms like Metropolis-Hastings) in a variety of applications. -- Provided by publisher.

This book constitutes the refereed proceedings of the 5th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2005, held in Lausanne, Switzerland in March/April 2005.The 24 revised full papers presented were carefully reviewed and selected from 66 submissions. The papers cover evolutionary algorithms as well as related approaches like scatter search, simulated annealing, ant colony optimization, immune algorithms, variable neighborhood search, hyperheuristics, and estimation of distribution algorithms. The papers deal with representations, analysis of operators and fitness landscapes, and comparison algorithms. Among the combinatorial optimization problems studied are graph coloring, quadratic assignment, knapsack, graph matching, packing, scheduling, timetabling, lot-sizing, and the traveling salesman problem.
(source: Nielsen Book Data)

• Introduction
• A Swarm Global Optimization Algorithm Inspired in the Behavior of the Social-spider.-A States of Matter Algorithm for Global Optimization
• An Algorithm for Global Optimization Inspired by Collective Animal Behavior
• A Bio-inspired Evolutionary Algorithm: Allostatic Optimization
• Optimization Based on the Behavior of Locust Swarms.

The goal of this book is to present advances that discuss alternative Evolutionary Computation (EC) developments and non-conventional operators which have proved to be effective in the solution of several complex problems. The book has been structured so that each chapter can be read independently from the others. The book contains nine chapters with the following themes: 1) Introduction, 2) the Social Spider Optimization (SSO), 3) the States of Matter Search (SMS), 4) the collective animal behavior (CAB) algorithm, 5) the Allostatic Optimization (AO) method, 6) the Locust Search (LS) algorithm, 7) the Adaptive Population with Reduced Evaluations (APRE) method, 8) the multimodal CAB, 9) the constrained SSO method.