Genetic Algorithms and Evolutionary Computation

By: Anil Menon (Editor)

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Hardcover | 29 February 2004

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Hardcover
296 Pages

Dimensions(cm)
23.5 x 15.88 x 2.54

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Frontiers of Evolutionary Computation brings together eleven contributions by international leading researchers discussing what significant issues still remain unresolved in the field of Evolutionary Computation (EC). They explore such topics as the role of building blocks, the balancing of exploration with exploitation, the modeling of EC algorithms, the connection with optimization theory and the role of EC as a meta-heuristic method, to name a few. The articles feature a mixture of informal discussion interspersed with formal statements, thus providing the reader an opportunity to observe a wide range of EC problems from the investigative perspective of world-renowned researchers. These prominent researchers include:
-Heinz M¼hlenbein,
-Kenneth De Jong,
-Carlos Cotta and Pablo Moscato,
-Lee Altenberg,
-Gary A. Kochenberger, Fred Glover, Bahram Alidaee and Cesar Rego,
-William G. Macready,
-Christopher R. Stephens and Riccardo Poli,
-Lothar M. Schmitt,
-John R. Koza, Matthew J. Street and Martin A. Keane,
-Vivek Balaraman,
-Wolfgang Banzhaf and Julian Miller.

Frontiers of Evolutionary Computation is ideal for researchers and students who want to follow the process of EC problem-solving and for those who want to consider what frontiers still await their exploration.

Industry Reviews

From the reviews:

"This is a diverse collection of eleven papers that share the common theme of posing open problems and pointing out possible directions of future research, all written by leading researchers in the field of evolutionary computation (EC). ... A good starting point for readers who are not familiar with evolutionary computation is provided ... . A different approach taken by several authors is the combination of EC with different fields with the aim of enriching and developing EC theory." (Thomas Jansen, Mathematical Reviews, 2005f)

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List of Figures
List of Tables
Preface
Contributing Authors
Towards a Theory of Organisms and Evolving Automata
Introduction
Evolutionary computation and theories of evolution
Darwin's continental cycle conjecture
The system view of evolution
Von Neumann's self-reproducing automata
Turing's intelligent machine
What can be computed by an artificial neural network?
Limits of computing and common sense
A logical theory of adaptive systems
The lambda-Calculus for creating artificial intelligence
Probabilistic logic
Stochastic analysis of cellular automata
Stochastic analysis of evolutionary algorithms
Stochastic analysis and symbolic representations
Conclusion
Two Grand Challenges for EC
Introduction
Historical Diversity
The Challenge of Unfication
The Challenge of Expansion
Summary and Conclusions
Evolutionary Computation: Challenges and duties
Introduction
Challenge #1: Hard problems for the paradigm - Epistasis and Parameterized Complexity
Challenge #2: Systematic design of provably good recombination operators
Challenge #3: Using Modal Logic and Logic Programming methods to guide the search
Challenge #4: Learning from other metaheuristics and other open challenges
Conclusions
Open Problems in the Spectral Analysis of Evolutionary Dynamics
Optimal Evolutionary Dynamics for Optimization
Spectra for Finite Population Dynamics
Karlin's Spectral Theorem for Genetic Operator Intensity
Conclusion
Solving Combinatorial Optimization Problems via Reformulation and Adaptive Memory Meta- heuristics
Introduction
Transformations
Examples
Solution Approaches
Computational Experience
Summary
Problems in Optimization
Introduction
Foundations
Connections
Applications
Conclusions
EC Theory - "In Theory"
Asymptotic Convergence of Scaled Genetic Algorithms
Notation and Preliminaries
The Genetic Operators
Convergence of Scaled Genetic Algorithms to Global Optima
Future Extensions of the Theory
Appendix: Proof of some basic or technical results
The Challenge of Producing Human-Competitive Results by Means of Genetic and Evolutionary Computation
Turing's Prediction Concerning Genetic and Evolutionary Computation
Definition of Human-Competitiveness
Desirable Attributes of the Pursuit of Human-Competitiveness
Human-Competitiveness as a Compass for Theoretical Work
Research Areas Supportive of Human-Competitive Results
Promising Application Areas for Genetic and Evolutionary Computation
Acknowledgements
Case Based Reasoning
Introduction
Case-Based Reasoning
Case Memory as an Evolutionary System
Hybrid Systems
Evolving Higher Levels
Conclusions
The Challenge Of Complexity
GP Basics and State of the Art
The Situation in Biology
Nature's way to deal with complexity
What we can learn from Nature?
A possible scenario: Transfer into Genetic Programming
Conclusion
Author Index
Table of Contents provided by Publisher. All Rights Reserved.

Genetic Algorithms and Evolutionary Computation

At a Glance

Hardcover

Industry Reviews

Shipping

How to return your order

Defective items

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