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https://doi.org/10.1007/s10957-005-5494-4

has been cited by the following article:

• TITLE: Generating Epsilon-Efficient Solutions in Multiobjective Optimization by Genetic Algorithm

  AUTHORS: El-Desouky Rahmo, Marcin Studniarski

  KEYWORDS: Vector Optimization, Approximate Solutions, Genetic Algorithm, Stopping Criteria

  JOURNAL NAME: Applied Mathematics, Vol.8 No.3, March 30, 2017

  ABSTRACT: We develop a new evolutionary method of generating epsilon-efficient solutions of a continuous multiobjective programming problem. This is achieved by discretizing the problem and then using a genetic algorithm with some derived probabilistic stopping criteria to obtain all minimal solutions for the discretized problem. We prove that these minimal solutions are the epsilon-optimal solutions to the original problem. We also present some computational examples illustrating the efficiency of our method.