A cellular genetic algorithm with self-adjusting acceptance threshold

We present a genetic algorithm (GA) whose population possesses a spatial structure. The GA is formulated as a probabilistic cellular automaton: The individuals are distributed over a connected graph and the genetic operators are applied locally in some neighborhood of each individual. By adding a self-organizing acceptance threshold schedule to the proportionate reproduction operator we can prove that the algorithm converges to the global optimum. First results for a multiple knapsack problem indicate a significant improvement in convergence behavior. The algorithm can be mapped easily onto parallel computers.