Scalable Speed-ups for the SMS-EMOA from a Simple Aging Strategy
Mingfeng Li, Weijie Zheng, Benjamin Doerr

TL;DR
This paper introduces an aging-based non-elitist selection mechanism for multi-objective evolutionary algorithms, demonstrating significant, objective-independent speed-ups in computing Pareto fronts, outperforming previous stochastic methods.
Contribution
It proposes a novel aging-based selection strategy that achieves faster convergence in SMS-EMOA, overcoming limitations of stochastic selection and providing objective-independent speed-ups.
Findings
Proves a speed-up factor of ((k)^{k-1}) regardless of objectives.
Demonstrates positive polynomial runtime speed-up for constant number of objectives.
Shows aging-based mechanisms outperform stochastic selection in efficiency.
Abstract
Different from single-objective evolutionary algorithms, where non-elitism is an established concept, multi-objective evolutionary algorithms almost always select the next population in a greedy fashion. In the only notable exception, Bian, Zhou, Li, and Qian (IJCAI 2023) proposed a stochastic selection mechanism for the SMS-EMOA and proved that it can speed up computing the Pareto front of the bi-objective jump benchmark with problem size and gap parameter by a factor of . While this constitutes the first proven speed-up from non-elitist selection, suggesting a very interesting research direction, it has to be noted that a true speed-up only occurs for , where the runtime is super-polynomial, and that the advantage reduces for larger numbers of objectives as shown in a later work. In this work, we propose a different non-elitist selection…
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Taxonomy
TopicsEmbedded Systems Design Techniques · Context-Aware Activity Recognition Systems
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
