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Task allocation for multiple-network architectures

Modular neural architectures pose the problem to find those subtasks of a complex task which can be efficiently trained together on the same network. We attack the involved combinatorial optimization problem by a genetic algorithm. For comparison a monolithic network and a modular architecture with random task distribution are considered. Letter recognition experiments show that the proposed method yields considerably better results concerning final convergence speed, generalization and completeness of solutions.