Non-Uniform Mapping in Binary-Coded Genetic Algorithms

Binary-coded genetic algorithms (BGAs) traditionally use a uniform mapping to decode strings to corresponding real-parameter variable values. In this paper, we suggest a non-uniform mapping scheme for creating solutions towards better regions in the search space, dictated by BGA’s population statistics. Both variable-wise and vector-wise non-uniform mapping schemes are suggested. Results on five standard test problems reveal that the proposed non-uniform mapping BGA (or NBGA) is much faster in converging close to the true optimum than the usual uniformly mapped BGA. With the base-line results, an adaptive NBGA approach is then suggested to make the algorithm parameterfree. Results are promising and should encourage further attention to non-uniform mapping strategies with binary coded GAs.