This review classifies genetic-algorithmenvironments into application-oriented systems, algorithm-oriented systems, and toolkits. It also presents detailed case studies of leading environments. Following Holland'...
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This review classifies genetic-algorithmenvironments into application-oriented systems, algorithm-oriented systems, and toolkits. It also presents detailed case studies of leading environments. Following Holland's (1975) original geneticalgorithm 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 programmingenvironments. 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 programmingenvironments
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