Real-coded Genetic Algorithms, Virtual Alphabets, and Blocking

This paper presents a theory of convergence for real coded genetic algorithms GAs that use oating point or other high cardinality codings in their chromosomes The theory is consistent with the theory of schemata and postulates that selection dominates early GA performance and restricts subsequent search to intervals with above average function value dimension by dimension These intervals may be further subdivided on the basis of their attraction under genetic hillclimbing Each of these subintervals is called a virtual character and the collection of characters along a given dimension is called a virtual alphabet It is the virtual alphabet that is searched during the recombinative phase of the genetic algorithm and in many problems this is su cient to ensure that good solutions are found Although the theory helps suggest why many problems have been solved using real coded GAs it also suggests that real coded GAs can be blocked from further progress in those situations when local optima separate the virtual characters from the global optimum

[1]  John H. Holland,et al.  Outline for a Logical Theory of Adaptive Systems , 1962, JACM.

[2]  H. Bremermann,et al.  AN EVOLUTION-TYPE SEARCH METHOD FOR CONVEX SETS. , 1964 .

[3]  Roger Weinberg,et al.  Computer simulation of a living cell , 1970 .

[4]  V. L. Anderson Evolutionary Operation : A Method for Increasing Industrial Productivity , 1970 .

[5]  N. Foo,et al.  Algebraic, geometric, and stochastic aspects of genetic operators , 1972 .

[6]  B. P. Zeigler,et al.  Comparison of genetic algorithms with conjugate gradient methods , 1972 .

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

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

[9]  Hans-Paul Schwefel,et al.  Numerical optimization of computer models , 1981 .

[10]  Lawrence Davis,et al.  Genetic Algorithms and Communication Link Speed Design: Theoretical Considerations , 1987, ICGA.

[11]  Lawrence Davis,et al.  Genetic Algorithms and Communication Link Speed Design: Constraints and Operators , 1987, ICGA.

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

[13]  O. G. Selfridge,et al.  Pandemonium: a paradigm for learning , 1988 .

[14]  J. David Schaffer,et al.  Representation and Hidden Bias: Gray vs. Binary Coding for Genetic Algorithms , 1988, ML.

[15]  Lawrence Davis,et al.  Training Feedforward Neural Networks Using Genetic Algorithms , 1989, IJCAI.

[16]  David E. Goldberg,et al.  Sizing Populations for Serial and Parallel Genetic Algorithms , 1989, ICGA.

[17]  Gerrit Kateman,et al.  Application of Genetic Algorithms in Chemometrics , 1989, ICGA.

[18]  David E. Goldberg,et al.  Zen and the Art of Genetic Algorithms , 1989, ICGA.

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

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

[21]  Kalyanmoy Deb,et al.  Messy Genetic Algorithms: Motivation, Analysis, and First Results , 1989, Complex Syst..

[22]  Kalyanmoy Deb,et al.  Messy Genetic Algorithms Revisited: Studies in Mixed Size and Scale , 1990, Complex Syst..

[23]  Alden H. Wright,et al.  Genetic Algorithms for Real Parameter Optimization , 1990, FOGA.

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