R. SirovetnukulP. ChutimaW. WattanapornpromP. ChongstitvatanaMahidol UniversityChulalongkorn University2018-05-032018-05-032011-12-01IEEE International Conference on Industrial Engineering and Engineering Management. (2011), 476-4812157362X215736112-s2.0-84856546222https://repository.li.mahidol.ac.th/handle/20.500.14594/11644Most 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. © 2011 IEEE.Mahidol UniversityBusiness, Management and AccountingEngineeringConference PaperSCOPUS10.1109/IEEM.2011.6117963