An Adaptive Crossover Operator for Genetic Algorithms to Solve the Optimization Problems

Abstract

Traditional genetic algorithms have been determined as an active approach for solving the optimization problems. Still now, most of GAs use a single crossover operator for all individuals. It means that all individuals in a population use the same mechanism due to this reason may be lack of intelligence for a particular individual; it is difficult to deal with complex situations. In adaptive scheme, single individual has four crossover operators to cope with different situations in the landscape. The adaptive algorithm can enable an individual to select the best operator according to its own local fitness landscape. The experimental results determine that our proposed technique is able to choose automatically the suitable crossover operator for different optimization problems.