  We propose an extended genetic algorithm (GA) with different local
environmental conditions. Genetic entities, or configurations, are put on nodes
in a ring structure, and location-dependent environmental conditions are
applied for each entity. Our GA is motivated by the geographic aspect of
natural evolution: Geographic isolation reduces the diversity in a local group,
but at the same time, can enhance intergroup diversity. Mating of genetic
entities across different environments can make it possible to search for broad
area of the fitness landscape. We validate our extended GA for finding the
ground state of three-dimensional spin-glass system and find that the use of
different environmental conditions makes it possible to find the lower-energy
spin configurations at relatively shorter computation time. Our extension of GA
belongs to a meta-optimization method and thus can be applied for a broad
research area in which finding of the optimal state in a shorter computation
time is the key problem.
