Population size matters: rigorous runtime results for maximizing the hypervolume indicator

DOI10.1016/j.tcs.2014.06.023zbMath1303.68123OpenAlexW2009656095MaRDI QIDQ477080

Andrew M. Sutton, Frank Neumann, Anh Quang Nguyen

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.2014.06.023

zbMATH Keywords

genetic programmingtheoryevolutionary multi-objective optimizationhypervolume indicatorruntime time analysis

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

Analysing the robustness of evolutionary algorithms to noise: refined runtime bounds and an example where noise is beneficial, Design and analysis of diversity-based parent selection schemes for speeding up evolutionary multi-objective optimisation, Mathematical runtime analysis for the non-dominated sorting genetic algorithm II (NSGA-II)

Uses Software

• SMS-EMOA
• DataModeler

Cites Work

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• Approximating the volume of unions and intersections of high-dimensional geometric objects
• Running time analysis of evolutionary algorithms on a simplified multiobjective knapsack problem
• Probability with Martingales
• Theory of the hypervolume indicator
• Computational complexity analysis of simple genetic programming on two problems modeling isolated program semantics
• Evolutionary Multi-Criterion Optimization