Diversity-Driven Self-adaptation in Evolutionary Algorithms

Abstract

Pareto-based multi-objective optimization problems (MOPs) are currently best solved using evolutionary algorithms. Nevertheless, the performance of these nature-inspired stochastic search algorithms still depends on the suitability of their parameter settings with respect to specific optimization problems. The tuning of the parameters is a crucial task which concerns resolving the contrary goals of convergence and diversity. To address this issue, we propose a diversity-driven self-adaptive mechanism (SAM) for the simulated binary crossover. This novel technique exploits and optimizes the balance between exploration and exploitation during the evolutionary process. This “explore first and exploit later” approach is addressed through the automated and dynamic adjustment of the distribution index of the simulated binary crossover (SBX) operator. We conducted a series of experiments where SAM is applied to the Non-dominated Sorting Genetic Algorithm to solve the Sphere, Rastrigin, and ZDT optimization problems. Our experimental results have shown that our proposed self adaptation mechanism can produce promising results for both single and multi-objective problem sets.