Reducing the Computational Cost in Multi-objective Evolutionary Algorithms by Filtering Worthless Individuals
This work addresses computational efficiency for users of evolutionary algorithms in optimization problems, but it is incremental as it builds on an existing method.
The paper tackled the high computational cost of multi-objective evolutionary algorithms by filtering out worthless individuals to reduce fitness function evaluations, achieving a performance reduction that is not tangible compared to the gains in efficiency.
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.