A Coarse-Grained Parallel Genetic Algorithm Employing Cluster Analysis for Multi-modal Numerical Optimisation

This paper describes a technique for improving the performance of parallel genetic algorithms on multi-modal numerical optimisation problems. It employs a cluster analysis algorithm to identify regions of the search space in which more than one sub-population is sampling. Overlapping clusters are merged in one sub-population whilst a simple derating function is applied to samples in all other sub-populations to discourage them from further sampling in that region. This approach leads to a better distribution of the search effort across multiple sub-populations and helps to prevent premature convergence. On the test problems used, significant performance improvements over the traditional island model implementation are realised.