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Megha

Genetic Algorithms GAs owe their name to an early emphasis on representing and manipulating individuals at the level of genotype. In Holland’s original work [Holl75], GAs were proposed to understand adaptation phenomena in both natural and artificial systems and they have three key features that distinguish themselves from other computational methods modeled on natural evolution: The use of bitstring for representation The use of crossover as the primary method for producing variants The use of proportional selection Genetic Algorithms are the most popular technique in evolutionary computation research. In the traditional genetic algorithm, the representation used is a fixed-length bit string. Each position in the string is assumed to represent a particular feature of an individual, and the value stored in that position represents how that feature is expressed in the solution. Usually, the string is “evaluated as a collection of structural features of a solution that have little or no interactions” [Ange96]. The analogy may be drawn directly to genes in biological organisms. Each gene represents an entity that is structurally independent of other genes.