High dimensional function optimization using a new genetic local search suitable for parallel computers

In this paper, we propose a new genetic local search named GLSDC (a genetic local search with distance independent diversity control) by extending the basic idea of DIDC (a genetic algorithm with distance independent diversity control) to coarse grained parallelization. GLSDC employs a local search method as a search operator. GLSDC also uses genetic operators, i.e., a crossover operator and a generation alternation model. However, in GLSDC, the crossover operator is not used as a search operator, but is used only for converging the population. GLSDC has an ability to find multiple optima simultaneously by stacking good individuals that have been found by the local search. Finding multiple optima is often required when we try to solve real world problems. The effectiveness of the proposed method is verified through numerical experiments on several high dimensional benchmark problems.