Foundations of Genetic Algorithms 2001 (FOGA 6)

Worth Martin, William Spears, Worthy N. Martin

• Publisher: Morgan Kaufmann
• Year: 2001
• Print ISBN:
  9781558607347
• eBook ISBN:
  9780080506876
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

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
• 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
• Towards a Theory of Strong Overgeneral Classifiers
• Evolutionary Optimization Through PAC Learning
• 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
• Author Index
• Key Word Index