An Approximated Domination Relationship based on Binary Classifiers for Evolutionary Multiobjective Optimization

Preselection is an important strategy to improve evolutionary algorithms' performance by filtering out unpromising solutions before fitness evaluations. This paper introduces a preselection strategy based on an approximated Pareto domination relationship for multiobjective evolutionary optimization. For each objective, a binary relation between each pair of solutions is constructed based on the current population, and a binary classifier is built based on the binary relation pairs. In this way, an approximated Pareto domination relationship can be defined. When new trial solutions are generated, the approximated Pareto domination is used to select promising solutions, which shall be evaluated by the real objective functions. The new preselection is integrated into two algorithms. The experimental results on two benchmark test suites suggest that the algorithms with preselection outperform their original ones.