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AN EVALUATION OF A CONSTRAINED MULTI-OBJECTIVE GENETIC ALGORITHM
2020
Journal of Scientific Perspectives
Real world optimization problems involve multiple conflicting objectives (such as minimizing cost while maximizing the quality of a product) and are subject to constraints (such as physical feasibility or budget limitations) which makes them interesting to solve. Over the last decades, evolutionary algorithms have been largely used in solving optimization problems in various fields of science. The aim of this study is to evaluate the performance of a constrained version of the Nondominated
doi:10.26900/jsp.4.011
fatcat:pstxblhv3vagdchg375k5wssra