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Operator Control Parameters and Fine Tuning of Genetic Algorithms (GAs)
Abstract
Genetic algorithms (GAs) are heuristic, blind (i.e., black box-based) search techniques. The internal working of GAs is complex and is opaque for the general practitioner. GAs are a set of interconnected procedures that consist of complex interconnected activity among parameters. When a naive GA practitioner tries to implement GA code, the first question that comes into the mind is what are the value of GA control parameters (i.e., various operators such as crossover probability, mutation probability, population size, number of generations, etc. will be set to run a GA code)? This chapter clears all the complexities about the internal interconnected working of GA control parameters. GA can have many variations in its implementation (i.e., mutation alone-based GA, crossover alone-based GA, GA with combination of mutation and crossover, etc.). In this chapter, the authors discuss how variation in GA control parameter settings affects the solution quality.
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