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• Front Cover; Foundations of Genetic Algorithms6; Copyright Page; Contents;
• Chapter 1. Introduction;
• Chapter 2. Overcoming Fitness Barriers in Multi-Modal Search Spaces;
• Chapter 3. Niches in NK-Landscapes;
• Chapter 4. New Methods for Tunable, Random Landscapes;
• Chapter 5. Analysis of Recombinative Algorithms on a Non-Separable Building-Block Problem;
• Chapter 6. Direct Statistical Estimation of GA Landscape Properties;
• Chapter 7. Comparing Population Mean Curves;
• Chapter 8. Local Performance of the ((/(I, () -ES in a Noisy Environment.

• Chapter 9. Recursive Conditional Scheme Theorem, Convergence and Population Sizing in Genetic Algorithms
• Chapter 10. Towards a Theory of Strong Overgeneral Classifiers;
• Chapter 11. Evolutionary Optimization through PAC Learning;
• Chapter 12. Continuous Dynamical System Models of Steady-State Genetic Algorithms;
• Chapter 13. Mutation-Selection Algorithm: A Large Deviation Approach;
• Chapter 14. The Equilibrium and Transient Behavior of Mutation and Recombination;
• Chapter 15. The Mixing Rate of Different Crossover Operators;
• Chapter 16. Dynamic Parameter Control in Simple Evolutionary Algorithms.

• Chapter 17. Local Search and High Precision Gray Codes: Convergence Results and Neighborhoods
• Chapter 18. Burden and Benefits of Redundancy; Author Index; Key Word Index.

Papers presented at the 2000 Foundations of Genetic Algorithms (FOGA-6) sixth biennial workshop held in Charlottesville, VA.

• Some combinatorial landscapes on which a genetic algorithm outperforms other stochastic iterative methods / Dave Corne and Peter Ross
• Maximum entropy analysis of genetic algorithm operators / Jonathan L. Shapiro and Adam Prügel-Bennett
• The ant colony metaphor for searching continuous design spaces / G. Bilchev and I.C. Parmee
• Broadcast based fitness sharing GA for conflict resolution among autonomous robots / Sadoyoshi Mikami, Yukinori Kakazu and Terence C. Fogarty
• An adaptive poly-parental recombination strategy / Jim Smith and T.C. Fogarty
• Neighbourhood seeding to reduce problem modality / A.J. Swann
• Specialized recombinative operators for timetabling problems / Edmund Burke, Dave Elliman and Rupert Weare
• The use of local search suggestion lists for improving the solution of timetable problems with evolutionary algorithms / Ben Paechter, Andrew Cumming and Henri Luchian
• Comparing genetic algorithms, simulated annealing, and stochastic hillclimbing on timetable problems / Peter Ross and Dave Corne
• Evolutionary learning in computational ecologies : an application to adaptive, distributed routing in communication networks / Brian Carse, Terence C. Fogarty, and Alistair Munro
• The radio link frequency assignment problem : a case study using genetic algorithms / A. Kapsalis [and others]
• Scheduling planned maintenance of the national grid / W.B. Langdon
• Genetic operators and constraint handling for pipe network optimization / Dragan A. Savic and Godfrey A. Walters
• A multi-objective approach to constrained optimisation of gas supply networks : COMOGA method / Patrick D. Surry, Nicholas J. Radcliffe and Ian D. Boyd
• Ternary decision diagram optimisation of Reed-Muller logic functions using a genetic algorithm for variable and simplification rule ordering / J.F. Miller, P. Thomson and P.V.G. Bradbeer
• An evolutionary algorithm for parametric array signal processing / Dekum Yang and Stuart Flockton
• Constraints on task and search complexity in GA + NN models of learning and adaptive behaviour / Mukesh J. Patel
• Load balancing application of the genetic algorithm in a nonstationary environment / Frank Vavak, Terence C. Fogarty and Phillip Cheng
• Exploring some commercial applications of genetic programming / Gerald Robinson and Paul McIlroy.

This volume is based on the Workshop on Evolutionary Computing held in Sheffield, U.K., in April 1995 under the sponsorship of the Society for the Study of Artificial Intelligence and Simulation of Behavior (AISB). The 18 full papers presented were selected during a post-workshop refereeing meeting and chosen from 32 submissions for the workshop. The papers are organized in sections on evolutionary computing theory and techniques, timetabling, routing and scheduling, optimization, signal processing and control, and genetic programming. The collection of papers has a certain bias towards real world applications of evolutionary computing and particularly genetic algorithms.

• Learning Classifier Systems: Looking Back and Glimpsing Ahead
• Knowledge Representations
• Analysis of Population Evolution in Classifier Systems Using Symbolic Representations
• Investigating Scaling of an Abstracted LCS Utilising Ternary and S-Expression Alphabets
• Evolving Fuzzy Rules with UCS: Preliminary Results
• Analysis of the System
• A Principled Foundation for LCS
• Revisiting UCS: Description, Fitness Sharing, and Comparison with XCS
• Mechanisms
• Analysis and Improvements of the Classifier Error Estimate in XCSF
• A Learning Classifier System with Mutual-Information-Based Fitness
• On Lookahead and Latent Learning in Simple LCS
• A Learning Classifier System Approach to Relational Reinforcement Learning
• Linkage Learning, Rule Representation, and the?-Ary Extended Compact Classifier System
• New Directions
• Classifier Conditions Using Gene Expression Programming
• Evolving Classifiers Ensembles with Heterogeneous Predictors
• Substructural Surrogates for Learning Decomposable Classification Problems
• Empirical Evaluation of Ensemble Techniques for a Pittsburgh Learning Classifier System
• Applications
• Technology Extraction of Expert Operator Skills from Process Time Series Data
• Analysing Learning Classifier Systems in Reactive and Non-reactive Robotic Tasks.

This book constitutes the thoroughly refereed joint post-conference proceedings of two consecutive International Workshops on Learning Classifier Systems that took place in Seattle, WA, USA in July 2006, and in London, UK, in July 2007 - all hosted by the Genetic and Evolutionary Computation Conference, GECCO. The 14 revised full papers presented were carefully reviewed and selected from the workshop contributions. The papers are organized in topical sections on knowledge representation, analysis of the system, mechanisms, new directions, as well as applications.

• Swarm Intelligence and Its Applications.- Operational Technologies and Networked Multimedia Applications.- Wearable Computing and Intelligent Data Hiding.- Image Processing and Intelligent Applications.- Intelligent Multimedia Tools and Applications.- Technologies for Next-Generation Network Environments.- Recent Progress in Computational Electromagnetic Dynamics.- Future Cyber Security, Privacy and Forensics for Advanced systems.- Data Mining Techniques and its Applications.- Optimization Models in Deep Learning/Machine Learning.
• (source: Nielsen Book Data)

This book contains selected papers presented at ICGEC 2021, the 14th International Conference on Genetic and Evolutionary Computing, held from October 21-23, 2021 in Jilin City, China. The conference was technically co-sponsored by Springer, Northeast Electric Power University Fujian University of Technology, Shandong University of Science and Technology, and Western Norway University of Applied Sciences. It is intended as an international forum for the researchers and professionals in all areas of genetic and evolutionary computing. And the readers may learn the up-to-date techniques of the mentioned topics, including swarm intelligence, artificial intelligence, information hiding and data mining techniques, which can help them to bring new ideas or apply the designed approaches from the collected papers to their professional jobs.
(source: Nielsen Book Data)

• Swarm Intelligence and Its Applications.- Operational Technologies and Networked Multimedia Applications.- Wearable Computing and Intelligent Data Hiding.- Image Processing and Intelligent Applications.- Intelligent Multimedia Tools and Applications.- Technologies for Next-Generation Network Environments.- Recent Progress in Computational Electromagnetic Dynamics.- Future Cyber Security, Privacy and Forensics for Advanced systems.- Data Mining Techniques and its Applications.
• (source: Nielsen Book Data)

This book gathers papers presented at the 13th International Conference on Genetic and Evolutionary Computing (ICGEC 2019), which was held in Qingdao, China, from 1st to 3rd, November 2019. Since it was established, in 2006, the ICGEC conference series has been devoted to new approaches with a focus on evolutionary computing. Today, it is a forum for the researchers and professionals in all areas of computational intelligence including evolutionary computing, machine learning, soft computing, data mining, multimedia and signal processing, swarm intelligence and security. The book appeals to policymakers, academics, educators, researchers in pedagogy and learning theory, school teachers, and other professionals in the learning industry, and further and continuing education. .
(source: Nielsen Book Data)

• Front Cover; Foundations of Genetic Algorithms6; Copyright Page; Contents;
• Chapter 1. Introduction;
• Chapter 2. Overcoming Fitness Barriers in Multi-Modal Search Spaces;
• Chapter 3. Niches in NK-Landscapes;
• Chapter 4. New Methods for Tunable, Random Landscapes;
• Chapter 5. Analysis of Recombinative Algorithms on a Non-Separable Building-Block Problem;
• Chapter 6. Direct Statistical Estimation of GA Landscape Properties;
• Chapter 7. Comparing Population Mean Curves;
• Chapter 8. Local Performance of the ((/(I, () -ES in a Noisy Environment

• Chapter 9. Recursive Conditional Scheme Theorem, Convergence and Population Sizing in Genetic Algorithms
• Chapter 10. Towards a Theory of Strong Overgeneral Classifiers;
• Chapter 11. Evolutionary Optimization through PAC Learning;
• Chapter 12. Continuous Dynamical System Models of Steady-State Genetic Algorithms;
• Chapter 13. Mutation-Selection Algorithm: A Large Deviation Approach;
• Chapter 14. The Equilibrium and Transient Behavior of Mutation and Recombination;
• Chapter 15. The Mixing Rate of Different Crossover Operators;
• Chapter 16. Dynamic Parameter Control in Simple Evolutionary Algorithms

• Chapter 17. Local Search and High Precision Gray Codes: Convergence Results and Neighborhoods
• Chapter 18. Burden and Benefits of Redundancy; Author Index; Key Word Index

Foundations of Genetic Algorithms, Volume 6 is the latest in a series of books that records the prestigious Foundations of Genetic Algorithms Workshops, sponsored and organised by the International Society of Genetic Algorithms specifically to address theoretical publications on genetic algorithms and classifier systems. Genetic algorithms are one of the more successful machine learning methods. Based on the metaphor of natural evolution, a genetic algorithm searches the available information in any given task and seeks the optimum solution by replacing weaker populations with stronger ones. Includes research from academia, government laboratories, and industry Contains high calibre papers which have been extensively reviewed Continues the tradition of presenting not only current theoretical work but also issues that could shape future research in the field Ideal for researchers in machine learning, specifically those involved with evolutionary computation.

• Theory.- Extremal Optimization Dynamics in Neutral Landscapes: The Royal Road Case.- Improving the Scalability of EA Techniques: A Case Study in Clustering.- Ant Colony Optimization.- MC-ANT: A Multi-Colony Ant Algorithm.- Applications.- Artificial Evolution for 3D PET Reconstruction.- A Hybrid Genetic Algorithm/Variable Neighborhood Search Approach to Maximizing Residual Bandwidth of Links for Route Planning.- Parallelization of an Evolutionary Algorithm on a Platform with Multi-core Processors.- On the Difficulty of Inferring Gene Regulatory Networks: A Study of the Fitness Landscape Generated by Relative Squared Error.- Combinatorial Optimization.- Memetic Algorithms for Constructing Binary Covering Arrays of Strength Three.- A Priori Knowledge Integration in Evolutionary Optimization.- Robotics.- On-Line, On-Board Evolution of Robot Controllers.- The Transfer of Evolved Artificial Immune System Behaviours between Small and Large Scale Robotic Platforms.- Multi-objective Optimization.- An Analysis of Algorithmic Components for Multiobjective Ant Colony Optimization: A Case Study on the Biobjective TSP.- Alternative Fitness Assignment Methods for Many-Objective Optimization Problems.- Genetic Programming.- Evolving Efficient List Search Algorithms.- Semantic Similarity Based Crossover in GP: The Case for Real-Valued Function Regression.- Genetic-Programming Based Prediction of Data Compression Saving.- Machine Learning.- On the Characteristics of Sequential Decision Problems and Their Impact on Evolutionary Computation and Reinforcement Learning.
• (source: Nielsen Book Data)

This LNCS volume contains the best papers presented at the 9th Conference on 1 Arti?cial Evolution, EA 2009, held in Strasbourg (France). Previous EA events took place in Tours (2007, LNCS 4926), Lille (2005, LNCS 3871), Marseille (2003, LNCS 2936), Le Creusot (2001, LNCS 2310), Dunkerque (1999, LNCS 1829), Nimes (1997, LNCS 1363), Brest (1995, LNCS 1063) and Toulouse (1994, LNCS 1063). For this ninth edition, authors were invited to present their originalwork r- evantto arti?cialevolution, including, butnotlimitedto:evolutionarycompu- tion, evolutionaryoptimization, co-evolution, arti?ciallife, populationdynamics, theory, algorithmicsand modeling, implementations, application of evolutionary paradigmstotherealworld(industry, biosciences, ...), otherbiologically-inspired paradigms (swarm, arti?cial ants, arti?cial immune systems, ...), memetic al- rithms, multi-objective optimization, constraint handling, parallel algorithms, dynamic optimization, machine learning and hybridization with other soft c- puting techniques. Submitted papers were reviewed by at least four members of the Inter- tional ProgramCommittee, which selected 23 papers to be presented during the conference out of 43 submissions. However, only 17 papers were included in the present volume, resulting in a 39.5% acceptance rate. We would like to thank the members of the Program Committee for their conscientious work, the authors for their greatly appreciated contributions, but also the members of the Organizing Committee who, once more, managed to put together a really enjoyable conference in Strasbourg. Finally, ?nancial and material support coming from ENSPS, Universit'ede Strasbourg, CNRS, R' egion Alsace and the EA association contributed to the success of the conference and helped to keep registration fees very low.
(source: Nielsen Book Data)

• Nature Inspired Constrained Optimization.- Recent Advances on Evolutionary Optimization Technologies.- Wearable Computing and Intelligent Data Hiding.- Image Processing and Intelligent Applications.- Intelligent Multimedia Tools and Applications.- Technologies for Next-Generation Network Environments.
• (source: Nielsen Book Data)

This volume of Advances in Intelligent Systems and Computing contains accepted papers presented at ICGEC 2014, the 8th International Conference on Genetic and Evolutionary Computing. The conference this year was technically co-sponsored by Nanchang Institute of Technology in China, Kaohsiung University of Applied Science in Taiwan, and VSB-Technical University of Ostrava. ICGEC 2014 is held from 18-20 October 2014 in Nanchang, China. Nanchang is one of is the capital of Jiangxi Province in southeastern China, located in the north-central portion of the province. As it is bounded on the west by the Jiuling Mountains, and on the east by Poyang Lake, it is famous for its scenery, rich history and cultural sites. Because of its central location relative to the Yangtze and Pearl River Delta regions, it is a major railroad hub in Southern China. The conference is intended as an international forum for the researchers and professionals in all areas of genetic and evolutionary computing.
(source: Nielsen Book Data)

• Foundation issues revisited
• Modeling genetic algorithms
• Deception and the building block hypothesis
• Convergence and genetic diversity
• Genetic operators and their analysis
• Machine learning.