On the convergence of multiobjective evolutionary algorithms |
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| Affiliation: | 1. Tocantins Federal University, Undergraduate Computation Sciences Course, ALC NO 14 (109 Norte), C.P. 114, CEP 77001-090 Palmas, Brazil;2. Rio de Janeiro Federal University, Computing and Systems Engineering Department, Caixa Postal 68511, CEP 21945-970 Rio de Janeiro, Brazil;3. Rio de Janeiro Federal Rural University, Technology and Languages Department, Rua Capito Chaves, N 60, Centro, Nova Iguau, Rio de Janeiro CEP 26221-010, Brazil;4. Ceará Federal University, Department of Statistics and Applied Mathematics, Campus do Pici, CEP 60455-760 Fortaleza, Brazil;1. Center of Intelligent Decision-making and Machine Learning, School of Management, Xi’an Jiaotong University, Xi’an 710049, Shaanxi, PR China;2. Institute of Computing Science, Poznan University of Technology, Piotrowo 2, Poznań 60–965, Poland |
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| Abstract: | We consider the usage of evolutionary algorithms for multiobjective programming (MOP), i.e. for decision problems with alternatives taken from a real-valued vector space and evaluated according to a vector-valued objective function. Selection mechanisms, possibilities of temporary fitness deterioration, and problems of unreachable alternatives for such multiobjective evolutionary algorithms (MOEAs) are studied. Theoretical properties of MOEAs such as stochastic convergence with probability 1 are analyzed. |
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