Dropout-Inspired Strategies For Enhanced Exploration In Evolutionary Algorithms

Abstract

Finding the right balance between exploration and exploitation is a crucial challenge in evolutionary algorithms (EAs), as it strongly impacts solution quality and the ability to escape local optima. While diversity-preserving mechanisms are commonly employed, high population diversity does not necessarily translate into improved performance across problem landscapes. Recent empirical evidence suggests that effective EA optimization largely depends on predominantly exploitative search dynamics, with exploration being most beneficial during the early iterations. Nevertheless, sporadic exploratory bursts may help reintroduce diversity and enhance solution quality. In this study, inspired by dropout regularization in neural networks (NNs), we introduce three mechanisms—and their variants— that stochastically deactivate entire individuals or subsets of decision variables with the expectation of enhancing exploration. We assess the effectiveness of dropout in genetic algorithms (GAs) on twelve single-objective and five multi-objective benchmark functions. We empirically demonstrate that per-variable stochastic deactivation, termed Individual Dropout (IDrop), significantly outperforms a standard GA on single-objective problems with exploitable global structure and improves convergence toward the theoretical Pareto front in multi-objective settings. We also show that, while dropout alone is not exempt from the curse of dimensionality, combinations of dropout mechanisms can partially alleviate this limitation in most of the tested functions.