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• 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.

March 1-3, 1995, San Diego, California Evolutionary programming is one of the predominate algorithms withing the rapidly expanding field of evolutionary computation. These edited contributions to the Fourth Annual Conference on Evolutionary Programming are by leading scientists from academia, industry, and defense. The papers describe both the theory and practical application of evolutionary programming, as well as other methods of evolutionary computation including evolution strategies, genetic algorithms, genetic programming, and cultural algorithms. Topics include :- Novel Areas of Evolutionary Programming and Evolution Strategies.- Evolutionary Computation with Medical Applications.- Issues in Evolutionary Optimization Pattern Discovery, Pattern Recognition, and System Identification.- Hierarchical Levels of Learning.- Self-Adaptation in Evolutionary Computation.- Morphogenic Evolutionary Computation.- Issues in Evolutionary Optimization.- Evolutionary Applications to VLSI and Part Placement.- Applications of Evolutionary Computation to Biology and Biochemistry Control.- Applications of Evolutionary Computation.- Genetic and Inductive Logic Programming.- Genetic Neural Networks.- The Future of Evolutionary Computation. A Bradford Book. Complex Adaptive Systems series.
(source: Nielsen Book Data)

March 1-3, 1995, San Diego, California Evolutionary programming is one of the predominate algorithms withing the rapidly expanding field of evolutionary computation. These edited contributions to the Fourth Annual Conference on Evolutionary Programming are by leading scientists from academia, industry, and defense. The papers describe both the theory and practical application of evolutionary programming, as well as other methods of evolutionary computation including evolution strategies, genetic algorithms, genetic programming, and cultural algorithms. Topics include :- Novel Areas of Evolutionary Programming and Evolution Strategies.- Evolutionary Computation with Medical Applications.- Issues in Evolutionary Optimization Pattern Discovery, Pattern Recognition, and System Identification.- Hierarchical Levels of Learning.- Self-Adaptation in Evolutionary Computation.- Morphogenic Evolutionary Computation.- Issues in Evolutionary Optimization.- Evolutionary Applications to VLSI and Part Placement.- Applications of Evolutionary Computation to Biology and Biochemistry Control.- Applications of Evolutionary Computation.- Genetic and Inductive Logic Programming.- Genetic Neural Networks.- The Future of Evolutionary Computation. A Bradford Book. Complex Adaptive Systems series.
(source: Nielsen Book Data)

March 1-3, 1995, San Diego, California Evolutionary programming is one of the predominate algorithms withing the rapidly expanding field of evolutionary computation. These edited contributions to the Fourth Annual Conference on Evolutionary Programming are by leading scientists from academia, industry, and defense. The papers describe both the theory and practical application of evolutionary programming, as well as other methods of evolutionary computation including evolution strategies, genetic algorithms, genetic programming, and cultural algorithms.Topics include :- Novel Areas of Evolutionary Programming and Evolution Strategies.- Evolutionary Computation with Medical Applications.- Issues in Evolutionary Optimization Pattern Discovery, Pattern Recognition, and System Identification.- Hierarchical Levels of Learning.- Self-Adaptation in Evolutionary Computation.- Morphogenic Evolutionary Computation.- Issues in Evolutionary Optimization.- Evolutionary Applications to VLSI and Part Placement.- Applications of Evolutionary Computation to Biology and Biochemistry Control.- Applications of Evolutionary Computation.- Genetic and Inductive Logic Programming.- Genetic Neural Networks.- The Future of Evolutionary Computation.A Bradford Book. Complex Adaptive Systems series

March 1-3, 1995, San Diego, California Evolutionary programming is one of the predominate algorithms withing the rapidly expanding field of evolutionary computation. These edited contributions to the Fourth Annual Conference on Evolutionary Programming are by leading scientists from academia, industry, and defense. The papers describe both the theory and practical application of evolutionary programming, as well as other methods of evolutionary computation including evolution strategies, genetic algorithms, genetic programming, and cultural algorithms. Topics include :- Novel Areas of Evolutionary Programming and Evolution Strategies.- Evolutionary Computation with Medical Applications.- Issues in Evolutionary Optimization Pattern Discovery, Pattern Recognition, and System Identification.- Hierarchical Levels of Learning.- Self-Adaptation in Evolutionary Computation.- Morphogenic Evolutionary Computation.- Issues in Evolutionary Optimization.- Evolutionary Applications to VLSI and Part Placement.- Applications of Evolutionary Computation to Biology and Biochemistry Control.- Applications of Evolutionary Computation.- Genetic and Inductive Logic Programming.- Genetic Neural Networks.- The Future of Evolutionary Computation. A Bradford Book. Complex Adaptive Systems series.
(source: Nielsen Book Data)

This volume contains the best carefully revised full papers selected from the presentations accepted for the AI '93 and AI '94 Workshop on Evolutionary Computation held in Australia. The 21 papers included cover a wide range of topics in the field of evolutionary computation, from constrained function optimization to combinatorial optimization, from evolutionary programming to genetic programming, from robotic strategy learning to co-evolutionary game strategy learning. The papers reflect important recent progress in the field; more than half of the papers come from overseas.
(source: Nielsen Book Data)

This volume presents a collection of revised refereed papers selected from the contributions presented at the European Conference on Artificial Evolution, AE '95, held in Brest, France, in September 1995; also included are a few papers from the predecessor conference, AE '94.Besides two invited surveys on evolution strategies and evolutionary programming, 24 full papers are presented. They are grouped into sections on evolutionary computation theory, genetic algorithm techniques, coevolution, neural networks, image processing, and applications to various optimization and other problems.
(source: Nielsen Book Data)

• PART I: A COMPARISON OF EVOLUTIONARY ALGORITHMS
• PART II: EXTENDING GENETIC ALGORITHMS.
• (source: Nielsen Book Data)

This book compares the three most prominent representatives of evolutionary algorithms - genetic algorithms, evolution strategies, and evolutionary programming - computational methods at the border between computer science and evolutionary biology. The algorithms are explained within a common formal framework, thereby clarifying the similarities and differences of these methods. The author also presents new results regarding the role of mutation and selection in genetic algorithms and uses a meta-evolutionary approach to confirm some of the theoretical results.
(source: Nielsen Book Data)

• PART I: A COMPARISON OF EVOLUTIONARY ALGORITHMS
• PART II: EXTENDING GENETIC ALGORITHMS.
• (source: Nielsen Book Data)

PART I: Comparison of Evolutionary Algorithms. 1. Organic Evolution and Problem Solving. 2. Specific Evolutionary Algorithms. 3. Artificial Landscapes. 4. An Empirical Comparison. PART II: Extending Genetic Algorithms. 5. Selection. 6. Mutation. 7. An Experiment in Meta-Evolution.
(source: Nielsen Book Data)
This book compares the three most prominent representatives of evolutionary algorithms - genetic algorithms, evolution strategies, and evolutionary programming - computational methods at the border between computer science and evolutionary biology. The algorithms are explained within a common formal framework, thereby clarifying the similarities and differences of these methods. The author also presents new results regarding the role of mutation and selection in genetic algorithms and uses a meta-evolutionary approach to confirm some of the theoretical results.
(source: Nielsen Book Data)

• PART I: A COMPARISON OF EVOLUTIONARY ALGORITHMS
• PART II: EXTENDING GENETIC ALGORITHMS.
• (source: Nielsen Book Data)

This book compares the three most prominent representatives of evolutionary algorithms - genetic algorithms, evolution strategies, and evolutionary programming - computational methods at the border between computer science and evolutionary biology. The algorithms are explained within a common formal framework, thereby clarifying the similarities and differences of these methods. The author also presents new results regarding the role of mutation and selection in genetic algorithms and uses a meta-evolutionary approach to confirm some of the theoretical results.
(source: Nielsen Book Data)

This book contains a selection of papers presented at a workshop on evolutionary computing sponsored by the Society for the Study of Artificial Intelligence and Simulation of Behaviour, AISB, at the University of Sussex in Brighton, UK, in April 1996.The 22 revised full papers included in the book, together with one invited contribution, were carefully reviewed by the program committee. Twelve contributions investigate applications of evolutionary computing in various areas, such as learning, scheduling, searching, genetic programming, image processing, and robotics. Eleven papers are devoted to evolutionary computing theory and techniques.
(source: Nielsen Book Data)

This book contains a selection of papers presented at a workshop on evolutionary computing sponsored by the Society for the Study of Artificial Intelligence and Simulation of Behaviour, AISB, at the University of Sussex in Brighton, UK, in April 1996.The 22 revised full papers included in the book, together with one invited contribution, were carefully reviewed by the program committee. Twelve contributions investigate applications of evolutionary computing in various areas, such as learning, scheduling, searching, genetic programming, image processing, and robotics. Eleven papers are devoted to evolutionary computing theory and techniques.
(source: Nielsen Book Data)

This book includes a selection of twelve carefully revised papers chosen from the papers accepted for presentation at the 4th IEEE/Nagoya-University World Wisepersons Workshop held in Nagoya in November 1995.The combining of the technologies of fuzzy logic, neural networks, and evolutionary computation is expected to open up a new paradigm of machine learning for the realization of human-like information generating systems. The excellent papers presented are organized in sections on fuzzy and evolutionary computation, fuzzy and learning automata, fuzzy and neural networks, genetic algorithms, and CAM-brain.
(source: Nielsen Book Data)

• Introduction.- Part I. Genetic Algorithms. GAs: What Are They? - GAs: How Do They Work? - GAs: Why Do They Work? - GAs: Selected Topics.- Part II. Numerical Optimization. Binary or Float? - Fine Local Tuning.- Handling Constraints.- Evolution Strategies and Other Methods.- Part III. Evolution Programs. The Transportation Problem.- The Traveling Salesman Problem.- Machine Learning.- Evolutionary Programming and Genetic Programming.- A Hierarchy of Evolution Programs.- Evolution Programs and Heuristics.- Conclusions.- Appendices.- References.- Index.
• (source: Nielsen Book Data)

Genetic algorithms are founded upon the principle of evolution, i.e., survival of the fittest. Hence evolution programming techniques, based on genetic algorithms, are applicable to many hard optimization problems, such as optimization of functions with linear and nonlinear constraints, the traveling salesman problem, and problems of scheduling, partitioning, and control. The importance of these techniques is still growing, since evolution programs are parallel in nature, and parallelism is one of the most promising directions in computer science.The book is self-contained and the only prerequisite is basic undergraduate mathematics. This third edition has been substantially revised and extended by three new chapters and by additional appendices containing working material to cover recent developments and a change in the perception of evolutionary computation.
(source: Nielsen Book Data)