Distributed GAs with case-based initial populations for real-time solution of combinatorial problems

Combinatorial problems are NP-complete, which means even infinite number of CPUs take polynomial time to search an optimal solution. Therefore approximate search algorithms such as Genetic Algorithms are used. However, such an approximate search algorithm easily falls into local optimum and just distributed / parallel processing seems inefficient. In this paper, we introduce distributed GAs, which compute their initial population in a case-based manner and compose their upcoming generations by the particular GAs, which exchange their solutions and make their individual decisions, when composing a next generation based on the fitness of the candidates and diversity issues.