Genetic Algorithms and the Design of Experiments

The genetic algorithm (GA) has most often been viewed from a biological perspective. The metaphors of natural selection, cross-breeding and mutation have been helpful in providing a framework in which to explain how and why they work. However, most practical applications of GAs are in the context of optimization, where alternative approaches may prove more effective. In attempting to understand how GAs function as optimizers, several alternative viewpoints have been suggested. In this paper we discuss one of these in some detail—one in which GAs are regarded as a form of sequential experimental design.

[1]  Yuval Davidor,et al.  Epistasis Variance: A Viewpoint on GA-Hardness , 1990, FOGA.

[2]  Yuval Davidor,et al.  Epistasis Variance: Suitability of a Representation to Genetic Algorithms , 1990, Complex Syst..

[3]  K. Dejong,et al.  An analysis of the behavior of a class of genetic adaptive systems , 1975 .

[4]  Susan E. Carlson,et al.  A Comparative Evaluation of Search Methods Applied to Catalog Selection , 1993, ICGA.

[5]  Colin R. Reeves,et al.  Genetic Algorithms versus Experimental Methods: A Case Study , 1997, International Conference on Genetic Algorithms.

[6]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[7]  D. Wolpert,et al.  On 2-Armed Gaussian Bandits and Optimization , 1996 .

[8]  Colin R. Reeves,et al.  An Experimental Design Perspective on Genetic Algorithms , 1994, FOGA.

[9]  Kenneth A. De Jong,et al.  Using Markov Chains to Analyze GAFOs , 1994, FOGA.

[10]  Alden H. Wright,et al.  Stability of Vertex Fixed Points and Applications , 1994, FOGA.

[11]  Colin R. Reeves,et al.  Epistasis in Genetic Algorithms: An Experimental Design Perspective , 1995, ICGA.

[12]  Albert Donally Bethke,et al.  Genetic Algorithms as Function Optimizers , 1980 .

[13]  L. Darrell Whitley,et al.  Fundamental Principles of Deception in Genetic Search , 1990, FOGA.

[14]  Heinz Mühlenbein,et al.  Evolution in Time and Space - The Parallel Genetic Algorithm , 1990, FOGA.

[15]  David E. Goldberg,et al.  Genetic Algorithms and Walsh Functions: Part I, A Gentle Introduction , 1989, Complex Syst..

[16]  Andrew J. Mason,et al.  Partition Coefficients, Static Deception and Deceptive Problems for Non-Binary Alphabets , 1991, ICGA.

[17]  Henry P. Wynn,et al.  Generalised confounding with Grobner bases , 1996 .

[18]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[19]  Colin R. Reeves,et al.  Using Genetic Algorithms with Small Populations , 1993, ICGA.

[20]  L. Darrell Whitley,et al.  An Executable Model of a Simple Genetic Algorithm , 1992, FOGA.

[21]  John H. Holland,et al.  When will a Genetic Algorithm Outperform Hill Climbing , 1993, NIPS.

[22]  Michael D. Vose,et al.  Modeling Simple Genetic Algorithms , 1992, FOGA.

[23]  Margaret J. Robertson,et al.  Design and Analysis of Experiments , 2006, Handbook of statistics.

[24]  Eva Riccomagno,et al.  Experimental Design and Observation for Large Systems , 1996, Journal of the Royal Statistical Society: Series B (Methodological).

[25]  David E. Goldberg,et al.  Genetic Algorithms and Walsh Functions: Part II, Deception and Its Analysis , 1989, Complex Syst..

[26]  T. J. Mitchell,et al.  Exploratory designs for computational experiments , 1995 .

[27]  Kennetb A. De.  Genetic Algorithms Are NOT Function Optimizers , 1992 .

[28]  John J. Grefenstette,et al.  Deception Considered Harmful , 1992, FOGA.

[29]  M. Denton,et al.  Evolution: A Theory in Crisis , 1985 .

[30]  Nicholas J. Radcliffe,et al.  Forma Analysis and Random Respectful Recombination , 1991, ICGA.