Analysis of diversity mechanisms for optimisation in dynamic environments with low frequencies of change

DOI10.1016/J.TCS.2014.10.028zbMath1303.68124OpenAlexW4253706688MaRDI QIDQ477083

Christine Zarges, Pietro S. Oliveto

Publication date: 2 December 2014

Published in: Theoretical Computer Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.tcs.2014.10.028

zbMATH Keywords

runtime analysispopulationsdiversity mechanismsevolutionary dynamic optimisation

Mathematics Subject Classification ID

Approximation methods and heuristics in mathematical programming (90C59) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Related Items (5)

A runtime analysis of parallel evolutionary algorithms in dynamic optimization ⋮ Populations can be essential in tracking dynamic optima ⋮ MMAS versus population-based EA on a family of dynamic fitness functions ⋮ Runtime analysis of RLS and \((1 + 1)\) EA for the dynamic weighted vertex cover problem ⋮ On the benefits and risks of using fitness sharing for multimodal optimisation

Cites Work

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• Evolutionary multi-objective optimization in uncertain environments. Issues and algorithms
• Comparison of simple diversity mechanisms on plateau functions
• On the analysis of the \((1+1)\) evolutionary algorithm
• Drift analysis and average time complexity of evolutionary algorithms

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