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D. Goldberg, B. Korb, K. Deb (1989)
Messy Genetic Algorithms: Motivation, Analysis, and First ResultsComplex Syst., 3
D. Goldberg (1987)
Simple Genetic Algorithms and the Minimal, Deceptive Problem
D. Goldberg (1988)
Genetic Algorithms in Search Optimization and Machine Learning
D. Goldberg (1989)
Genetic Algorithms and Walsh Functions: Part I, A Gentle IntroductionComplex Syst., 3
D. Goldberg (1989)
Genetic Algorithms and Walsh Functions: Part II, Deception and Its AnalysisComplex Syst., 3
D. Goldberg (1992)
Construction of high-order deceptive functions using low-order Walsh coefficientsAnnals of Mathematics and Artificial Intelligence, 5
D. Goldberg (1989)
Genetic Algorithms in Search
G. Liepins, M. Vose (1990)
Representational issues in genetic optimizationJ. Exp. Theor. Artif. Intell., 2
L. Whitley (1990)
Fundamental Principles of Deception in Genetic Search
M. Rudnick, D. Goldberg (1991)
Signal, noise, and genetic algorithms
J. Holland (1975)
Adaptation in natural and artificial systems
J. Grefenstette, J. Baker (1989)
How Genetic Algorithms Work: A Critical Look at Implicit Parallelism
(1980)
Bethke , Genetic algorithms as function optimizers , Ph . D . Dissertation , Department of Computer and Communication Science , University of Michigan (
G. Syswerda (1989)
Uniform Crossover in Genetic Algorithms
J. Fitzpatrick, J. Grefenstette (1988)
Genetic algorithms in noisy environmentsMachine Learning, 3
A. Bethke (1980)
Genetic Algorithms as Function Optimizers
D. Goldberg (1990)
IlliGAL Report No. 90002
M. Rudnick, D. Goldberg (1991)
IlliGAL Report 91004
This paper presents several theorems concerning the nature of deception, its relationship to hyperplane dominance, and the central role that deception plays in function optimization using genetic algorithms. The theoretical results relate to four general themes. First, the concept of a “deceptive attractor” is introduced; it is shown that a deceptive attractor must be the complement of the global solution for a problem to be fully deceptive. It is also shown that the deceptive attractor must either be a local optimum in Hamming space, or adjacent to a local optimum in Hamming space if the problem is fully deceptive. Second, it can be shown that the global solution to nondeceptive problems can be inferred (theoretically and often in practice) by determining the “winners” of the order-1 hyperplanes. The third theme relates the concept of deception and dominance. If a dominance relationship exists between two hyperplanes then deception is impossible between those two partitions of hyperspace; analogously, deception between two hyperplanes precludes a dominance relationship. The fourth theme relates to deception and implicit parallelism. It can be shown that if a genetic algorithm reliably allocates exponentially more trials to the observed best, then implicit parallelism (and the 2-arm bandit analogy) breaks down when deception is present.
Annals of Mathematics and Artificial Intelligence – Springer Journals
Published: Apr 5, 2005
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