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• Genesis
• motivation
• prediction experiments
• pattern recognition and classification
• control system design
• extension of early evolutionary programming concepts
• competitive goal-seeking
• some implications
• diversification
• two-person gaming against nonminimax players
• coevolution, pursuit and evasion
• modeling time series
• pattern recognition
• simulated ecosystems and the nature of intelligence
• sequence induction with deterministic automata
• revising and extending early evolutionary programming
• routing problems
• comparing crossover, inversion, and mutation
• specialisations
• finding structure in data
• self-adaptation
• evolving neural networks
• evolving S-expression and multiple interacting programs
• games
• other applications.
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A unique, one-stop reference to the history, technology, and application of evolutionary programming Evolutionary programming has come a long way since Lawrence Fogel first proposed in 1961 that intelligence could be modeled on the natural process of evolution. Efforts to apply this innovative approach to artificial intelligence have also evolved over the years, and the advent of fast desktop computers capable of solving complex computational problems has spawned an explosion of interest in the field. Offering the unique perspective of one of the inventors of evolutionary programming, this remarkable work traces forty years of developments in the field. Dr. Fogel consolidates a wealth of information and hard-to-find figures from across the literature, providing comprehensive coverage of the evolutionary programming approach to simulated evolution. This includes both an updated, condensed version of his bestselling 1966 work, Artificial Intelligence Through Simulated Evolution (with Owens and Walsh), and a thorough discussion of the history, technology, and methods of machine learning from 1970 to the present. This important resource features clear, up-to-date explanations of how the simulation of evolutionary processes allows machines to learn to solve new problems in new ways. And it helps readers make the leap to generating intelligent systems-extending the discussion to neural networks, fuzzy logic, and genetic algorithms development. Engineers and computer scientists in all areas of machine learning will gain invaluable insight into existing and emerging applications and obtain ample ideas to draw upon in future research.
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• Complexity formalisms, order and disorder in the structure of art.- Searching real-valued synaptic weights of Hopfield's associative memory using evolutionary programming.- The application of evolutionary computation to selected problems in molecular biology.- PEPNet: Parallel evolutionary programming for constructing artificial neural networks.- Scaling behavior of the evolution strategy when evolving neuronal control architectures for autonomous agents.- Swarm: An object oriented simulation platform applied to markets and organizations.- An agent-based computational model for the evolution of trade networks.- Performance enhanced genetic programming.- Comparing subtree crossover with macromutation.- Musica ex machina: Composing 16th-century counterpoint with genetic programming and symbiosis.- Design of a high-gain operational amplifier and other circuits by means of genetic programming.- Modeling speculators with genetic programming.- Fast evolution strategies.- Airspace congestion smoothing by stochastic optimization.- Evolian: Evolutionary optimization based on lagrangian with constraint scaling.- Solving static and dynamic fuzzy constraint networks using evolutionary hill-climbing.- Applying family competition to evolution strategies for constrained optimization.- Multi-operator evolutionary programming: A preliminary study on function optimization.- Supporting polyploidy in genetic algorithms using dominance vectors.- An individually variable mutation-rate strategy for genetic algorithms.- Inductive learning of mutation step-size in evolutionary parameter optimization.- A note on the escape probabilities for two alternative methods of selection under Gaussian mutation.- Raising theoretical questions about the utility of genetic algorithms.- Some geometric and algebraic results on crossover.- An analysis of evolutionary algorithms based on neighbourhood and step sizes.- Structuring pattern generalization through evolutionary techniques.- A cultural algorithm framework to evolve multiagent cooperation with evolutionary programming.- Tracking extrema in dynamic environments.- The dynamics of evolution strategies in the optimization of traveling salesman problems.- Exploring self-adaptive methods to improve the efficiency of generating approximate solutions to traveling salesman problems using evolutionary programming.- Optimizing fuel distribution through evolutionary programming.- Investigating parallel genetic algorithms on job shop scheduling problems.- Using evolutionary programming for finite element problems.- Gaining insight into evolutionary programming through landscape visualization: An investigation into IIR filtering.- Evolution of intelligently interactive behaviors for simulated forces.- Combining robot control strategies using genetic algorithms with memory.- Using cultural algorithm with evolutionary computing to extract site location decisions from spatio-temporal databases.
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This book constitutes the refereed proceedings of the 6th International Conference on Evolutionary Programming, EP 97, held in Indianapolis, IN, USA, in April 1997. The 36 revised full papers presented were carefully selected for inclusion in the proceedings. The papers are organized in sections on evolutionary methods for modeling and training, alternative frameworks for the computational study of evolutionary social systems, genetic programming: issues and applications, issues in evolutionary optimization, enhanced evolutionary operators, theory and analysis of evolutionary computations, issues in adaptability: theory and practice, and evolution and NP-hard problems.
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This book constitutes the refereed proceedings of six workshops on evolutionary computation held concurrently as EvoWorkshops 2000 in Edinburgh, Scotland, UK, in April 2000. The 37 revised papers presented were carefully reviewed and selected by the respective program committees. All in all, the book demonstrates the broad application potential of evolutionary computing in a variety of fields. In accordance with the individual workshops, the book is divided into sections on image and signal processing; systems, controls, and drives in industry; telecommunications; scheduling and timetabling; robotics; and aeronautics.
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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.
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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.
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• pt. I. Exploiting multiple objectives : from problems to solutions
• pt. II. Machine learning with multiple objectives
• pt. III. Multiple objectives in design and engineering
• pt. IV. Scaling up multiobjective optimization.

• Basic Concepts.- MOP Evolutionary Algorithm Approaches.- MOEA Local Search and Coevolution.- MOEA Test Suites.- MOEA Testing and Analysis.- MOEA Theory and Issues.- Applications.- MOEA Parallelization.- Multi-Criteria Decision Making.- Alternative Metaheuristics.
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This textbook is a second edition of Evolutionary Algorithms for Solving Multi-Objective Problems, significantly expanded and adapted for the classroom. The various features of multi-objective evolutionary algorithms are presented here in an innovative and student-friendly fashion, incorporating state-of-the-art research. The book disseminates the application of evolutionary algorithm techniques to a variety of practical problems. It contains exhaustive appendices, index and bibliography and links to a complete set of teaching tutorials, exercises and solutions.
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• Practical Genetic Algorithms.- Real-World Application: Routing Problem.- Elitist Compact Genetic Algorithms.- Real-coded Bayesian Optimization Algorithm.- Multiobjective Real-coded Bayesian Optimization Algorithm.- Conclusions.
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Genetic and evolutionary algorithms (GEAs) have often achieved an enviable success in solving optimization problems in a wide range of disciplines. This book provides effective optimization algorithms for solving a broad class of problems quickly, accurately, and reliably by employing evolutionary mechanisms.
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This book provides a clear and comprehensive introduction to the field of evolutionary computation that takes an integrated approach. Evolutionary computation, the use of evolutionary systems as computational processes for solving complex problems, is a tool used by computer scientists and engineers who want to harness the power of evolution to build useful new artefacts, by biologists interested in developing and testing better models of natural evolutionary systems, and by artificial life scientists for designing and implementing new artificial evolutionary worlds. In this clear and comprehensive introduction to the field, Kenneth De Jong presents an integrated view of the state of the art in evolutionary computation. Although other books have described such particular areas of the field as genetic algorithms, genetic programming, evolution strategies, and evolutionary programming, "Evolutionary Computation" is noteworthy for considering these systems as specific instances of a more general class of evolutionary algorithms. This useful overview of a fragmented field is suitable for classroom use or as a reference for computer scientists and engineers.
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• Linking Entropy to Estimation of Distribution Algorithms.- Entropy-based Convergence Measurement in Discrete Estimation of Distribution Algorithms.- Real-coded Bayesian Optimization Algorithm.- The CMA Evolution Strategy: A Comparing Review.- Estimation of Distribution Programming: EDA-based Approach to Program Generation.- Multi-objective Optimization with the Naive
• ID A.- A Parallel Island Model for Estimation of Distribution Algorithms.- GA-EDA: A New Hybrid Cooperative Search Evolutionary Algorithm.- Bayesian Classifiers in Optimization: An EDA-like Approach.- Feature Ranking Using an EDA-based Wrapper Approach.- Learning Linguistic Fuzzy Rules by Using Estimation of Distribution Algorithms as the Search Engine in the COR Methodology.- Estimation of Distribution Algorithm with 2-opt Local Search for the Quadratic Assignment Problem.
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Estimation of Distribution Algorithms (EDAs) are a set of algorithms in the Evolutionary Computation (EC) field characterized by the use of explicit probability distributions in optimization. Contrarily to other EC techniques such as the broadly known Genetic Algorithms (GAs) in EDAs, the crossover and mutation operators are substituted by the sampling of a distribution previously learnt from the selected individuals. EDAs have experienced a high development that has transformed them into an established discipline within the EC field. This book attracts the interest of new researchers in the EC field as well as in other optimization disciplines, and that it becomes a reference for all of us working on this topic. The twelve chapters of this book can be divided into those that endeavor to set a sound theoretical basis for EDAs, those that broaden the methodology of EDAs and finally those that have an applied objective.
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• Setting the stage for structured populations
• Island models
• Island models: empirical properties
• Lattice cellular models
• Lattice cellular models: empirical properties
• Random and irregular cellular populations
• Coevolutionary structured models
• Some nonconventional models
• Implementation notes.

• Setting the stage for structured populations
• Island models
• Island models: empirical properties
• Lattice cellular models
• Lattice cellular models: empirical properties
• Random and irregular cellular populations
• Coevolutionary structured models
• Some nonconventional models
• Implementation notes.

As information handling systems get more and more complex, it becomes increasingly difficult to manage them using traditional approaches based on centralized and pre-defined control mechanisms. Over recent years, there has been a significant increase in taking inspiration from biology, the physical world, chemistry, and social systems to more efficiently manage such systems - generally based on the concept of self-organisation; this gave rise to self-organising applications. This book constitutes a reference and starting point for establishing the field of engineering self-organising applications. It comprises revised and extended papers presented at the Engineering Self-Organising Applications Workshop, ESOA 2003, held at AAMAS 2003 in Melbourne, Australia, in July 2003 and selected invited papers from leading researchers in self-organisation. The book is organized in parts on applications, natural metaphors (multi-cells and genetic algorithms, stigmergy, and atoms and evolution), artificial interaction mechanisms, middleware, and methods and tools.
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Evolutionary Algorithms, in particular Evolution Strategies, Genetic Algorithms, or Evolutionary Programming, have found wide acceptance as robust optimization algorithms in the last ten years. Compared with the broad propagation and the resulting practical prosperity in different scientific fields, the theory has not progressed as much. This monograph provides the framework and the first steps toward the theoretical analysis of Evolution Strategies (ES). The main emphasis is on understanding the functioning of these probabilistic optimization algorithms in real-valued search spaces by investigating the dynamical properties of some well-established ES algorithms. The book introduces the basic concepts of this analysis, such as progress rate, quality gain, and self-adaptation response, and describes how to calculate these quantities. Based on the analysis, functioning principles are derived, aiming at a qualitative understanding of why and how ES algorithms work.
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• Glossary.- Part I. Setting the Stage: Introduction.- Background.- Part II. Static Theoretical Analyses: A Survival Schema Theory for Recombination.- A Construction Schema Theory for Recombination.- Survival and Construction Schema Theory for Recombination.- A Survival Schema Theory for Mutation.- A Construction Schema Theory for Mutation.- Schema Theory: Mutation versus Recombination.- Other Static Characterizations of Mutation and Recombination.- Part III. Dynamic Theoretical Analyses: Dynamic Analyses of Mutation and Recombination.- A Dynamic Model of Selection and Mutation.- A Dynamic Model of Selection, Recombination and Mutation.- An Aggregation Algorithm for Markov Chains.- Part IV. Empirical Analyses: Empirical Validation.- Part V. Summary: Summary and Discussion.- Appendix: Formal Computations for the Aggregation Algorithm.- Bibliography.
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Despite decades of work in evolutionary algorithms, there remains a lot of uncertainty as to when it is beneficial or detrimental to use recombination or mutation. This book provides a characterization of the roles that recombination and mutation play in evolutionary algorithms. It integrates prior theoretical work and introduces new theoretical techniques for studying evolutionary algorithms. An aggregation algorithm for Markov chains is introduced which is useful for studying not only evolutionary algorithms specifically, but also complex systems in general. Practical consequences of the theory are explored and a novel method for comparing search and optimization algorithms is introduced. A focus on discrete rather than real-valued representations allows the book to bridge multiple communities, including evolutionary biologists and population geneticists.
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• METHODOLOGICAL ASPECTS. Using Genetic Algorithms for Optimization: Technology Transfer in Action (J. Haataja). An Introduction to Evolutionary Computation and Some Applications (D. Fogel). Evolutionary Computation: Recent Developments and Open Issues (K. De Jong). Some Recent Important Foundational Results in Evolutionary Computation (D. Fogel). Evolutionary Algorithms for Engineering Applications (Z. Michalewicz, et al.). Embedded Path Tracing and Neighbourhood Search Techniques (C. Reeves T. Yamada). Parallel and Distributed Evolutionary Algorithms (M. Tomassini). Evolutionary Multi-Criterion Optimization (K. Deb). ACO Algorithms for the Traveling Salesman Problem (T. St?tzle M. Dorigo). Genetic Programming: Turing's Third Way to Achieve Machine Intelligence (J. Koza, et al.). Automatic Synthesis of the Topology and Sizing for Analog Electrical Circuits Using Genetic Programming (F. Bennett, et al.). APPLICATION-ORIENTED APPROACHES. Multidisciplinary Hybrid Constrained GA Optimization (G. Dulikravich, et al.). Genetic Algorithm as a Tool for Solving Electrical Engineering Problems (M. Rudnicki, et al.). Genetic Algorithms in Shape Optimization: Finite and Boundary Element Applications (M. Cerrolaza W. Annicchiarico). Genetic Algorithms and Fractals (E. Lutton). Three Evolutionary Approaches to Clustering (H. Luchian). INDUSTRIAL APPLICATIONS. Evolutionary Algorithms Applied to Academic and Industrial Test Cases (T. B ck, et al.). Optimization of an Active Noise Control System Inside an Aircraft, Based on the Simultaneous Optimal Positioning of Microphones and Speakers, with the Use of a Genetic Algorithm (Z. Diamantis, et al.). Generator Scheduling in Power Systems by Genetic Algorithm and Expert System (B. Galvan, et al.). Efficient Partitioning Methods for 3-D Unstructured Grids Using Genetic Algorithms (A. Giotis, et al.). Genetic Algorithms in Shape Optimization of a Paper Machine Headbox A Parallel Genetic Algorithm for Multi-Objective Optimization in Computational Fluid Dynamics (N. Marco, et al.). Application of a Multi Objective Genetic Algorithm and a Neural Network to the Optimisation of Foundry Processes (G. Meneghetti, et al.). Circuit Partitioning Using Evolution Algorithms (J. Montiel-Nelson, et al.).
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Evolutionary Algorithms in Engineering and Computer Science Edited by K. Miettinen, University of Jyvaskyla, Finland M. M. Makela, University of Jyvaskyla, Finland P. Neittaanmaki, University of Jyvaskyla, Finland J. Periaux, Dassault Aviation, France What is Evolutionary Computing? Based on the genetic message encoded in DNA, and digitalized algorithms inspired by the Darwinian framework of evolution by natural selection, Evolutionary Computing is one of the most important information technologies of our times. Evolutionary algorithms encompass all adaptive and computational models of natural evolutionary systems - genetic algorithms, evolution strategies, evolutionary programming and genetic programming. In addition, they work well in the search for global solutions to optimization problems, allowing the production of optimization software that is robust and easy to implement. Furthermore, these algorithms can easily be hybridized with traditional optimization techniques. This book presents state-of-the-art lectures delivered by international academic and industrial experts in the field of evolutionary computing. It bridges artificial intelligence and scientific computing with a particular emphasis on real-life problems encountered in application-oriented sectors, such as aerospace, electronics, telecommunications, energy and economics. This rapidly growing field, with its deep understanding and assesssment of complex problems in current practice, provides an effective, modern engineering tool. This book will therefore be of significant interest and value to all postgraduates, research scientists and practitioners facing complex optimization problems.
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• 1: Fundamentals
• 1.1. Evolutionary Algorithms
• 1.2. On the Combination of Fuzzy Logic and Evolutionary Computation: A Short Review and Bibliography
• 1.13. Fuzzy/Multiobjective Genetic Systems for Intelligent Systems Design Tools and Components
• 2: Methodology and Algorithms
• 2.1. GA Algorithms in Intelligent Robots
• 2.2. Development of If-Then Rules with the Use of DNA Coding
• 2.3. Genetic-Algorithm-Based Approaches to Classification Problems
• 2.4. Multiobjective Fuzzy Satisficing Methods for 0-1 Knapsack Problems through Genetic Algorithms
• 2.5. Multistage Evolutionary Optimization of Fuzzy Systems
• Application to Optimal Fuzzy Control
• 2.6. Evolutionary Learning in Neural Fuzzy Control Systems
• 2.7. Stable Identification and Adaptive Control
• A Dynamic Fuzzy Logic System Approach
• 2.8. Evolutionary Based Learning of Fuzzy Controllers
• 2.9. GA-Based Generation of Fuzzy Rules
• 3: Bibliography
• 3.1. An Indexed Bibliography of Genetic Algorithms with Fuzzy Logic.

The main theme of Fuzzy Evolutionary Computation is to highlight a synergistic effect that is emerging between fuzzy sets and evolutionary computation. This volume discusses and quantifies the main advantages arising from this new symbiosis. The scope of the book is broad, ranging from coverage of fundamental ideas in fuzzy sets and evolutionary computation, through inclusion of cutting edge research, to case studies. The focus is on the applied side of fuzzy evolutionary calculations. Each contribution is systematic and thorough in its presentations, and emphasizes design of evolutionary schemes that embraces various sources of domain knowledge. The authors have also included problem sets at the end of each chapter which explore specific conceptual and algorithmic points covered in the text. Fuzzy Evolutionary Computation is an indispensable reference work for practitioners, engineers, and scientists interested in techniques of evolutionary computation in the context of fuzzy sets and/or global optimization. The book will be useful for individuals actively pursuing research applications in both fuzzy sets and evolutionary computation.

• Part 1: Fundamentals. 1.1. Evolutionary Algorithms-- Z. Michalewicz, et al. 1.2. On the Combination of Fuzzy Logic And Evolutionary Computation: A Short Review and Bibliography-- O. Cordon, et al. 1.3. Fuzzy/Multiobjective Genetic Systems for Intelligent Systems Design Tools and Components-- M.A. Lee, H. Esbensen. Part 2: Methodology and Algorithms. 2.1. GA Algorithms in Intelligent Robots-- T. Fukuda, et al. 2.2. Development of If-Then Rules with the Use of DNA Coding-- T. Furuhashi. 2.3. Genetic-Algorithm-Based Approaches to Classification Problems-- H. Ishibuchi, et al. 2.4. Multiobjective Fuzzy Satisficing Methods for 0-1 Knapsack Problems Through Genetic Algorithms-- M. Sakawa, T. Shibano. 2.5. Multistage Evolutionary Optimization of Fuzzy Systems
• Application to Optimal Fuzzy Control-- J. Kacprzyk. 2.6. Evolutionary Learning in Neural Fuzzy Control Systems-- D.A. Linkens, H.O. Nyongesa. 2.7. Stable Identification and Adaptive Control
• A Dynamic Fuzzy Logic System Approach-- G. Vukovich, J.X. Lee. 2.8. Evolutionary Based Learning of Fuzzy Controllers-- L. Magdalena, J.R. Velasco. 2.9. GA-Based Generation of Fuzzy Rules-- O. Nelles. Part 3: Bibliography. 3.1. An Indexed Bibliography of Genetic Algorithms with Fuzzy Logic-- J.T. Alander.
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The main theme of this text is to highlight a synergistic effect that is emerging between fuzzy sets and evolutionary computation. This quantifies the main advantage arising from the symbiosis. The scope of the book is broad, ranging from coverage of fundamental ideas in fuzzy sets and evolutionary computation, through inclusion of cutting-edge research, to case studies. The focus is on the applied side of fuzzy evolutionary calculations. Each contribution is systematic and thorough in its presentations, and emphasizes design of evolutionary schemes that embraces various sources of domain knowledge. The authors have also included problem sets at the end of each chapter which explore specific conceptual and algorithmic points covered in the text.
(source: Nielsen Book Data)