A Multi-parent Crossover Based Genetic Algorithm for Bi-Objective Unconstrained Binary Quadratic Programming Problem
نویسندگان
چکیده
In this paper, we present a multi-parent crossover based genetic algorithm for the bi-objective unconstrained binary quadratic programming problem, by integrating the multi-parent crossover within the framework of hypervolume-based multi-objective optimization algorithm. The proposed algorithm employs a multi-parent crossover operator to generate the offspring solutions, which are used to further improve the quality of Pareto approximation set. Experimental results on 10 benchmark instances demonstrate the efficacy of our proposed algorithm compared with the original multi-objective optimization algorithms.
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