Adaptive non-uniform crossover based on statistics for genetic algorithms

The genetic algorithm (GA) is a meta-heuristic search algorithm based on mechanisms abstracted from population genetics. Through the population, the GA implicitly maintains the statistics about the search space. This implicit statistics can be used explicitly to enhance GA's performance. In this paper, a new statistics-based adaptive non-uniform crossover (SANUX) is proposed. SANUX uses the statistics information of the allele distribution in each locus to adaptively adjust the crossover operation. Our preliminary experiment results show that SANUX is more efficient than traditional one-point, two-point, and uniform crossover across a representative set of search problems.