Proposal for a Novel Method for Crossover Operators in Genetic Algorithms: The Sigmoid Crossover”,

ABSTRACT Genetic Algorithms (GAs) are a set of local search algorithms that are based on principles of biology and are usually applied to evolutionary systems. However, the efficacy of GAs depend on the crossover operators used and the fitness of the individuals in the population. Current crossover operators are applied to individuals based on a constant probability along with their fitness. Eventually, after a large number of generations, the individuals converge as the same chromosome survives, which may lead to local and not global optimizations of the problem concerned In this paper, the problems with the fixed constant value of the crossover probability approach are discussed, an alternative solution based on a sigmoid probability distribution is proposed, data sets are compiled and presented for a comparative analysis and finally, it is shown that the new approach does help preserve "genetic diversity" of the population of solutions, thus giving the population a better chance at finding a global optimu