Stochastic Population Update Can Provably Be Helpful in Multi-Objective Evolutionary Algorithms
IJCAI 2023(2023)
摘要
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. That is,
the next-generation population is formed by selecting the best solutions from
the current population and newly-generated solutions (irrespective of the
selection criteria used such as Pareto dominance, crowdedness and indicators).
In this paper, we question this practice. 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 challenge a common practice in the
design of existing MOEAs. Its positive results, which might hold more
generally, should encourage the exploration of developing new MOEAs in the
area.
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关键词
stochastic population update,multi-objective
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