Reducing the Computational Cost in Multi-objective Evolutionary Algorithms by Filtering Worthless Individuals

TL;DR

This paper proposes a filtering method to reduce the number of fitness evaluations in multi-objective evolutionary algorithms, aiming to lower computational costs while maintaining acceptable performance levels.

Contribution

It introduces a novel filtering approach integrated into NSGA-II to more accurately discard worthless individuals, reducing evaluations and computational effort.

Findings

Significant reduction in fitness evaluations achieved.

Performance loss is minimal despite fewer evaluations.

Method outperforms baseline in benchmark tests.

Abstract

The large number of exact fitness function evaluations makes evolutionary algorithms to have computational cost. In some real-world problems, reducing number of these evaluations is much more valuable even by increasing computational complexity and spending more time. To fulfill this target, we introduce an effective factor, in spite of applied factor in Adaptive Fuzzy Fitness Granulation with Non-dominated Sorting Genetic Algorithm-II, to filter out worthless individuals more precisely. Our proposed approach is compared with respect to Adaptive Fuzzy Fitness Granulation with Non-dominated Sorting Genetic Algorithm-II, using the Hyper volume and the Inverted Generational Distance performance measures. The proposed method is applied to 1 traditional and 1 state-of-the-art benchmarks with considering 3 different dimensions. From an average performance view, the results indicate that although decreasing the number of fitness evaluations leads to have performance reduction but it is not tangible compared to what we gain.