Genetic-algorithm programming environments

This review classifies genetic-algorithm environments into application-oriented systems, algorithm-oriented systems, and toolkits. It also presents detailed case studies of leading environments. Following Holland's (1975) original genetic algorithm proposal, many variations of the basic algorithm have been introduced. However. an important and distinctive feature of all GAs is the population-handling technique. The original GA adopted a generational replacement policy, according to which the whole population is replaced in each generation. Conversely, the steady-state policy used by many subsequent GAs selectively replaces the population. After we introduce GA models and their programming, we present a survey of GA programming environments. We have grouped them into three major classes according to their objectives: application-oriented systems hide the details of GAs and help users develop applications for specific domains; algorithm-oriented systems are based on specific GA models; and toolkits are flexible environments for programming a range of GAs and applications. We review the available environments and describe their common features and requirements. As case studies, we select some specific systems for more detailed examination. To conclude, we discuss likely future developments in GA programming environments.<<ETX>>

[1]  Reiko Tanese,et al.  Distributed Genetic Algorithms , 1989, ICGA.

[2]  D. E. Goldberg,et al.  Genetic Algorithms in Search, Optimization & Machine Learning , 1989 .

[3]  Ingo Rechenberg,et al.  Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution , 1973 .

[4]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

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

[6]  Nicol N. Schraudolph,et al.  A User's Guide to GAucsd 1.4 , 1992 .

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

[8]  Martina Gorges-Schleuter,et al.  ASPARAGOS An Asynchronous Parallel Genetic Optimization Strategy , 1989, ICGA.

[9]  Darrell Whitley,et al.  Genitor: a different genetic algorithm , 1988 .

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

[11]  John Holland,et al.  Adaptation in Natural and Artificial Sys-tems: An Introductory Analysis with Applications to Biology , 1975 .

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

[13]  L. Wang.  SPLICER - A GENETIC ALGORITHM TOOL FOR SEARCH AND OPTIMIZATION, VERSION 1.0 (MACINTOSH VERSION) , 1994 .

[14]  Heinz Mühlenbein,et al.  Parallel Genetic Algorithms, Population Genetics, and Combinatorial Optimization , 1989, Parallelism, Learning, Evolution.

[15]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[16]  Thomas Bäck,et al.  Genetic Algorithms and Evolution Strategies - Similarities and Differences , 1990, PPSN.

[17]  H. P. Schwefel,et al.  Numerische Optimierung von Computermodellen mittels der Evo-lutionsstrategie , 1977 .

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

[19]  W. Vent,et al.  Rechenberg, Ingo, Evolutionsstrategie — Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. 170 S. mit 36 Abb. Frommann‐Holzboog‐Verlag. Stuttgart 1973. Broschiert , 1975 .