The effectiveness of hybrid negative correlation learning in evolutionary algorithm for combinatorial optimization problems

Most evolutionary algorithms optimize the information from good solutions found in the population. A selection method discards the below-average solutions assuming that they do not contribute any information to update the probabilistic models. This work develops an algorithm called Coincidence algorithm (COIN) which merges negative correlation learning into the optimization process. A knight´s tour problem, one of NP-hard multimodal Hamiltonian path problems, is tested with COIN. The results show that COIN is a competitive algorithm in converging to better solutions and maintaining diverse solutions to solve combinatorial optimization problems.