Noisy multiobjective optimization on a budget of 250 evaluations

Joshua Knowles, David Corne, Alan Reynolds

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


    We consider methods for noisy multiobjective optimization, specifically methods for approximating a true underlying Pareto front when function evaluations are corrupted by Gaussian measurement noise on the objective function values. We focus on the scenario of a limited budget of function evaluations (100 and 250), where previously it was found that an iterative optimization method - ParEGO - based on surrogate modeling of the multiobjective fitness landscape was very effective in the non-noisy case. Our investigation here measures how ParEGO degrades with increasing noise levels. Meanwhile we introduce a new method that we propose for limited-budget and noisy scenarios: TOMO, deriving from the single-objective PB1 algorithm, which iteratively seeks the basins of optima using nonparametric statistical testing over previously visited points. We find ParEGO tends to outperform TOMO, and both (but especially ParEGO), are quite robust to noise. TOMO is comparable and perhaps edges ParEGO in the case of budgets of 100 evaluations with low noise. Both usually beat our suite of five baseline comparisons.

    Original languageEnglish
    Title of host publicationEvolutionary Multi-Criterion Optimization - 5th International Conference, EMO 2009, Proceedings
    Number of pages15
    Publication statusPublished - 2010
    Event5th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2009 - Nantes, France
    Duration: 7 Apr 200910 Apr 2009

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume5467 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    Conference5th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2009


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