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Geiringer theorems: from population genetics to computational intelligence, memory evolutive systems and Hebbian learning
2013
Natural Computing
The classical Geiringer theorem addresses the limiting frequency of occurrence of various alleles after repeated application of crossover. It has been adopted to the setting of evolutionary algorithms and, a lot more recently, reinforcement learning and Monte-Carlo tree search methodology to cope with a rather challenging question of action evaluation at the chance nodes. The theorem motivates novel dynamic parallel algorithms that are explicitly described in the current paper for the first
doi:10.1007/s11047-013-9395-4
fatcat:y5q4t57frbh6hd2x2nwkhybky4