Mitigating Selection Bias in Local Optima: A Meta-Analysis of Niching Methods in Continuous Optimization
Junchen Wang, Changhe Li, Yiya Diao · Information · 2025
As mainstream solvers for black-box optimization problems, evolutionary computation (EC) methods struggle with finding desired optima of lower attractiveness. Researchers have designed benchmark problems for simulating this scenario and proposed a large number of niching methods for solving those problems. However, factors causing the difference in attractiveness between local optima are often coupled in existing benchmark problems, which makes it hard to clarify the primary contributors. In addition, niching methods are carried out using a combination of several niching techniques and reproduction operators, which enhances the difficulty of identifying the essential effects of different niching techniques. To obtain an in-depth understanding of the above issue, thus offering actionable insights for optimization tasks challenged by the multimodality, this paper uses continuous optimization as an entry point and focuses on analyzing differential behaviors of EC methods across different basins of attraction. Specifically, we quantitatively investigate the independent impacts of three features of basins of attraction via corresponding benchmark scenarios generated by Free Peaks. The results show that the convergence biases induced by the difference in distribution only occur in EC methods with less uniform reproduction operators. On the other hand, convergence biases induced by differences in size and average fitness, both of which equate to the difference in size of superior region, pose a challenge to any EC method driven by objective functions. As niching methods limit survivor selection to specified neighborhoods to mitigate the latter biases, we abstract five niching techniques from these methods by their definitions of neighborhood for restricted competition, thus identifying key parameters that govern their efficacy. Experiments confirm these parameters’ critical roles in reducing convergence biases.