An Analysis of the Universal Suffrage Selection Operator

The universal suffrage selection operator, designed primarily for concept learning inside the system REGAL, is discussed for both overlapping and nonoverlapping populations. Analysis of its behavior is performed by using the virtual average population method, a new tool for investigating asymptotic properties of convergence of macroscopic quantities related to the population of a genetic algorithm.

[1]  Kalyanmoy Deb,et al.  A Comparative Analysis of Selection Schemes Used in Genetic Algorithms , 1990, FOGA.

[2]  Kenneth A. De Jong,et al.  Learning Concept Classification Rules Using Genetic Algorithms , 1991, IJCAI.

[3]  Jukka Hekanaho,et al.  Symbiosis in Multimodal Concept Learning , 1995, ICML.

[4]  Heinz Mühlenbein,et al.  The Science of Breeding and Its Application to the Breeder Genetic Algorithm (BGA) , 1993, Evolutionary Computation.

[5]  Jeffrey Horn,et al.  Finite Markov Chain Analysis of Genetic Algorithms with Niching , 1993, ICGA.

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

[7]  Attilio Giordana,et al.  Learning Structured Concepts Using Genetic Algorithms , 1992, ML.

[8]  William M. Spears,et al.  Simple Subpopulation Schemes , 1998 .

[9]  Filippo Neri,et al.  A Parallel Genetic Algorithm for Concept Learning , 1995, ICGA.

[10]  Carol A. Ankenbrandt An Extension to the Theory of Convergence and a Proof of the Time Complexity of Genetic Algorithms , 1990, FOGA.

[11]  Kalyanmoy Deb,et al.  An Investigation of Niche and Species Formation in Genetic Function Optimization , 1989, ICGA.

[12]  Adam Prügel-Bennett,et al.  A Statistical Mechanical Formulation of the Dynamics of Genetic Algorithms , 1994, Evolutionary Computing, AISB Workshop.

[13]  John J. Grefenstette Predictive Models Using Fitness Distributions of Genetic Operators , 1994, FOGA.

[14]  Morgan B Kaufmann,et al.  Finite Markov Chain Analysis of Genetic Algorithms with Niching , 1993 .

[15]  Filippo Neri,et al.  Search-Intensive Concept Induction , 1995, Evolutionary Computation.

[16]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .

[17]  Peter J. B. Hancock,et al.  An Empirical Comparison of Selection Methods in Evolutionary Algorithms , 1994, Evolutionary Computing, AISB Workshop.

[18]  Gilles Venturini,et al.  SIA: A Supervised Inductive Algorithm with Genetic Search for Learning Attributes based Concepts , 1993, ECML.

[19]  Georges R. Harik,et al.  Finding Multimodal Solutions Using Restricted Tournament Selection , 1995, ICGA.

[20]  David E. Goldberg,et al.  Finite Markov Chain Analysis of Genetic Algorithms , 1987, ICGA.

[21]  David E. Goldberg,et al.  Implicit Niching in a Learning Classifier System: Nature's Way , 1994, Evolutionary Computation.

[22]  Lorenza Saitta,et al.  Learning Disjunctive Concepts by Means of Genetic Algorithms , 1994, ICML.

[23]  Samir W. Mahfoud Niching methods for genetic algorithms , 1996 .

[24]  Andrew McCallum,et al.  Using Genetic Algorithms to Learn Disjunctive Rules from Examples , 1990, ML.

[25]  Bruce Tidor,et al.  An Analysis of Selection Procedures with Particular Attention Paid to Proportional and Boltzmann Selection , 1993, International Conference on Genetic Algorithms.

[26]  Károly F. Pál,et al.  Selection Schemes with Spatial Isolation for Genetic Optimization , 1994, PPSN.

[27]  David E. Goldberg,et al.  Genetic Algorithms with Sharing for Multimodalfunction Optimization , 1987, ICGA.

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

[29]  Dirk Thierens,et al.  Convergence Models of Genetic Algorithm Selection Schemes , 1994, PPSN.

[30]  Filippo Neri,et al.  Analysis of Genetic Algorithms Evolution under Pure Selection , 1995, ICGA.