Hot off the Press: Stochastic Population Update Can Provably Be Helpful in Multi-Objective Evolutionary Algorithms
TL;DR
This work analytically present that stochastic population update can be beneficial for the search of MOEAs, and proves that the expected running time of two well-established MOEAs, SMS-EMOA and NSGA-II, for solving two bi-objective problems can be exponentially decreased if replacing its deterministic population update mechanism by a stochastic one.
Abstract
Evolutionary algorithms (EAs) have been widely and successfully applied to solve multi-objective optimization problems, due to their nature of population-based search. Population update, a key component in multi-objective EAs (MOEAs), is usually performed in a greedy, deterministic manner. In this paper, we analytically present that stochastic population update can be beneficial for the search of MOEAs. Specifically, we prove that the expected running time of two well-established MOEAs, SMS-EMOA and NSGA-II, for solving two bi-objective problems, OneJumpZeroJump and bi-objective RealRoyalRoad, can be exponentially decreased if replacing its deterministic population update mechanism by a stochastic one. Empirical studies also verify the effectiveness of the proposed population update method. This work is an attempt to show the benefit of introducing randomness into the population update of MOEAs. Its positive results, which might hold more generally, should encourage the exploration of developing new MOEAs in the area. This paper for the Hot-off-the-Press track at GECCO 2025 summarizes the work C. Bian, Y. Zhou, M. Li, and C. Qian. Stochastic Population Update Can Provably Be Helpful in Multi-Objective Evolutionary Algorithms. Artificial Intelligence, 2025, 341: 104308. [5]