Multi-Objective Genetic Algoirthms With Concepts from Statistical Thermodynamics
نویسندگان
چکیده
Obtaining the fullest possible representation of solutions to a multi-objective optimization problem has been a major concern in MultiObjective Genetic Algorithms (MOGAs). This is because a MOGA, due to its nature, usually produces several clusters of solutions that does not cover the whole range of Pareto frontier. This poster paper indroduces an overview of a new approach, one that aims at obtaining a Pareto solution set with maximum possible coverage and uniformiy. The proposed algorithm is based on an application of the concepts from statistical theory of gases (i.e., entropy) to a MOGA.
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