Genetic Algorithms
(GAs) emulate the
natural evolution process and maintain a population of potential
solutions to a given problem. Through the population, GAs implicitly
maintain the statistics about the search space. This implicit
statistics can be used explicitly to enhance GA's performance.
Inspired by this idea, a statistics-based adaptive non-uniform
crossover (SANUX) has been proposed. SANUX
uses the
statistics information of the alleles in each locus to adaptively
calculate the swapping probability of that locus for crossover
operation. A simple triangular function has been used to calculate
the swapping probability. In this paper two new functions, the
trapezoid and exponential functions, are proposed for SANUX instead
of the triangular function. Experiment results show that both
functions further improve the performance of SANUX.