Different environmental conditions in genetic algorithm

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 conditions applied for each entity. Our GA is motivated by the geographic aspect of natural evolution: Geographic isolation reduces diversity group, but at same time, can enhance intergroup diversity. Mating entities across environments make it possible to search broad area fitness landscape. validate our finding ground state three-dimensional spin-glass system find that use makes lower-energy spin configurations relatively shorter computation time. extension belongs meta-optimization method thus be research which optimal time key problem.