Crossover operators with adaptive probability

Genetic algorithms (GAs) are adaptive methods, which can be employed to solve search and optimization problems. The GA relies on genetic operators to exchange gene between individuals for generating better offspring. An important issue to execute GA efficiently is to maintain population diversity and to sustain local improvement in the search stage. However, both effects always hinder each other. We propose to apply different kinds of crossover operators, i.e. arithmetic and BLX-α crossovers, to control the diversity and convergence of the GA in continuous-space framework. We also utilize self-adaptation method to control the probability of crossover such that the balance of exploitation and exploration can be kept. It is shown empirically that the proposed methods outperform the classical GA strategy on several benchmark functions