Book description
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
Table of contents
- Cover image
- Title page
- Table of Contents
- Copyright page
- FOGA-2000: The Program Committee
- Introduction
-
Overcoming Fitness Barriers in Multi-Modal Search Spaces
- Abstract
- 1 INTRODUCTION
- 2 PRELIMINARY OBSERVATION OF CYCLIC PHASE BEHAVIOUR IN PERFORMANCE PROFILES
- 3 THE H-IFF PERFORMANCE PROFILE
- 4 PHASES AND MUTATION EVENTS
- 5 ‘BEST FOUND’ FITNESS DISTRIBUTIONS
- 6 FURTHER EXPERIMENTS: KAUFFMAN NK, ROYAL STAIRCASE AND MAX-ONES
- 7 DISCUSSION
- 8 CONCLUSIONS
- Acknowledgements
- Niches in NK-Landscapes
- New Methods for Tunable, Random Landscapes
- Analysis of recombinative algorithms on a non-separable building-block problem
- Direct Statistical Estimation of GA Landscape Properties
- Comparing population mean curves
- Local Performance of the (μ/μI, λ)-ES in a Noisy Environment
-
Recursive Conditional Schema Theorem, Convergence and Population Sizing in Genetic Algorithms
- Abstract
- 1 INTRODUCTION
- 2 SOME ASSUMPTIONS AND DEFINITIONS
- 3 PROBABILISTIC SCHEMA THEOREMS WITHOUT EXPECTED VALUES
- 4 CONDITIONAL SCHEMA THEOREMS
- 5 A POSSIBLE ROUTE TO PROVING GA CONVERGENCE
- 6 RECURSIVE CONDITIONAL SCHEMA THEOREM
- 7 CONDITIONAL CONVERGENCE PROBABILITY
- 8 POPULATION SIZING
- 9 CONCLUSIONS AND FUTURE WORK
- Acknowledgements
- Towards a Theory of Strong Overgeneral Classifiers
-
Evolutionary Optimization Through PAC Learning
- Abstract
- 1 Introduction
- 2 Motivation
- 3 PAC Learning Preliminaries
- 4 Why We Should Work in a PAC Setting
- 5 PAC Learning Applied to Evolutionary Optimization
- 6 Transformation of a Representation
- 7 Evolutionary Operators
- 8 Finding High Correlation Parity Strings
- 9 Dimension Reduction
- 10 The Rising Tide Algorithm
- 11 Empirical Results
- 12 Discussion and Speculation
- 13 Conclusion
- Continuous Dynamical System Models of Steady-State Genetic Algorithms
- Mutation-Selection Algorithm: a Large Deviation Approach
- The Equilibrium and Transient Behavior of Mutation and Recombination
- The Mixing Rate of Different Crossover Operators
- Dynamic Parameter Control in Simple Evolutionary Algorithms
- Local Search and High Precision Gray Codes: Convergence Results and Neighborhoods
-
Burden and Benefits of Redundancy
- Abstract
- 1 INTRODUCTION
- 2 REASONS FOR REDUNDANCY
- 3 INVESTIGATED PROBLEMS AND METHODOLOGY
- 4 ANALYSIS I: DEGREE OF REDUNDANCY
- 5 ANALYSIS II: MUTATION AND THE STRUCTURE OF LANDSCAPE
- 6 ANALYSIS III: RECOMBINATION
- 7 ANALYSIS IV: DIVERSITY
- 8 ANALYSIS V: BENEFITS OF DIPLOIDITY – A CONTROL EXPERIMENT
- 9 CONCLUSION AND DISCUSSION
- Acknowledgements
- Appendix Fitness computation and problem
- Author Index
- Key Word Index
Product information
- Title: Foundations of Genetic Algorithms 2001 (FOGA 6)
- Author(s):
- Release date: July 2001
- Publisher(s): Morgan Kaufmann
- ISBN: 9780080506876
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