Hybrid Dynamic Resampling Algorithms for Evolutionary Multi-objective Optimization of Invariant-Noise Problems
2016 (English)In: Applications of Evolutionary Computation: 19th European Conference, EvoApplications 2016, Porto, Portugal, March 30 – April 1, 2016, Proceedings, Part II / [ed] Giovanni Squillero, Paolo Burelli, 2016, Vol. 9598, 311-326 p.Conference paper (Refereed)
In Simulation-based Evolutionary Multi-objective Optimization (EMO) the available time for optimization usually is limited. Since many real-world optimization problems are stochastic models, the optimization algorithm has to employ a noise compensation technique for the objective values. This article analyzes Dynamic Resampling algorithms for handling the objective noise. Dynamic Resampling improves the objective value accuracy by spending more time to evaluate the solutions multiple times, which tightens the optimization time limit even more. This circumstance can be used to design Dynamic Resampling algorithms with a better sampling allocation strategy that uses the time limit. In our previous work, we investigated Time-based Hybrid Resampling algorithms for Preference-based EMO. In this article, we extend our studies to general EMO which aims to find a converged and diverse set of alternative solutions along the whole Pareto-front of the problem. We focus on problems with an invariant noise level, i.e. a flat noise landscape.
Place, publisher, year, edition, pages
2016. Vol. 9598, 311-326 p.
Lecture Notes in Computer Science, ISSN 0302-9743 ; 9598
Evolutionary multi-objective optimization, Simulationbased optimization, Noise, Dynamic resampling, Budget allocation, Hybrid
Robotics Information Systems
Research subject Natural sciences; Technology
IdentifiersURN: urn:nbn:se:his:diva-12074DOI: 10.1007/978-3-319-31153-1_21ScopusID: 2-s2.0-84962257415ISBN: 978-3-319-31152-4ISBN: 978-3-319-31153-1OAI: oai:DiVA.org:his-12074DiVA: diva2:915353
19th European Conference, EvoApplications 2016, Porto, Portugal, March 30 – April 1, 2016