| 摘要 |
The present invention is directed to the use of an evolutionary algorithm to locate optimal solution subspaces. The evolutionary algorithm uses a point-based coding of the subspace determination problem and searches selectively over the space of possible coded solutions. Each feasible solution to the problem, or individual in the population of feasible solutions, is coded as a string, which facilitates use of the evolutionary algorithm to determine the optimal solution to the fitness function. The fitness of each string is determined by solving the objective function for that string. The resulting fitness value can then be converted to a rank, and all of the members of the population of solutions can be evaluated using selection, crossover, and mutation processes that are applied sequentially and iteratively to the individuals in the population of solutions. The population of solutions is updated as the individuals in the population evolve and converge, that is become increasingly genetically similar to one another. The iterations of selection, crossover and mutation are performed until a desired level of convergence among the individuals in the population of solutions has been achieved.
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