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Title : Evaluation of Crossover operators performance in Genetic Algorithms

Authors : S. Preethi Saroj     Volume 1 Issue 1    Pages: 8 - 25

ABSTRACT - Genetic Algorithms are implemented in search and optimization techniques that were developed based on ideas and techniques from genetic and evolutionary theory. Beginning with a random population of chromosomes, a genetic algorithm chooses parents from which to generate offspring using operations like selection, crossover and mutation. Here, comparisons of 5 crossover operators that are used in genetic algorithms are performed. In performance of a genetic algorithm, crossover operator has an invaluable role. It is necessary to understand the role of the crossover operator. The purpose of this project is to compare larger set of crossover operators on the same test functions and evaluate their efficiency. It also includes evaluation of statistical tests in order to study the performance of the crossover operator.

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