By dividing the solution space into several subspaces and performing search restricted to individual subspace has the advantage that effort in one subspace will not be repeated in the other subspace. This feature of exhaustive search is combined with evolutionary computation in each subspace via an adaptive allocation of computational resource to subspace search. A recent version of Genetic algorithm, called MOGA is used as the evolutionary computation. Chromosomes evolve in a given subspace only. The computational resource allocation will be based on the quality of search results: the subspace expected to contain the true solution will be given more computational resource. In this way, a quasi-parallelism is provided to evolutionary computation in different subspace in terms of computa...[ Read more ]

By dividing the solution space into several subspaces and performing search restricted to individual subspace has the advantage that effort in one subspace will not be repeated in the other subspace. This feature of exhaustive search is combined with evolutionary computation in each subspace via an adaptive allocation of computational resource to subspace search. A recent version of Genetic algorithm, called MOGA is used as the evolutionary computation. Chromosomes evolve in a given subspace only. The computational resource allocation will be based on the quality of search results: the subspace expected to contain the true solution will be given more computational resource. In this way, a quasi-parallelism is provided to evolutionary computation in different subspace in terms of computational time. Various ways of resource allocation have been tried on several problems. Results show that in general, division of solution space into subspace provides a higher efficiency. A similar technique is applied for genetic programming and experiments show that it also improve the efficiency of the program.