Noisy Multiobjective Optimization on a Budget of 250 Evaluations View Full Text


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Chapter Info

DATE

2009

AUTHORS

Joshua Knowles , David Corne , Alan Reynolds

ABSTRACT

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. More... »

PAGES

36-50

References to SciGraph publications

  • 2006-03. Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models in JOURNAL OF GLOBAL OPTIMIZATION
  • 2008. Multi-objective Model Predictive Optimization using Computational Intelligence in ARTIFICIAL INTELLIGENCE IN THEORY AND PRACTICE II
  • 2004. Metamodel Assisted Multiobjective Optimisation Strategies and their Application in Airfoil Design in ADAPTIVE COMPUTING IN DESIGN AND MANUFACTURE VI
  • 2005. Multiobjective Optimization on a Budget of 250 Evaluations in EVOLUTIONARY MULTI-CRITERION OPTIMIZATION
  • 2002-10-04. Bayesian Optimization Algorithms for Multi-objective Optimization in PARALLEL PROBLEM SOLVING FROM NATURE — PPSN VII
  • 1998-12. Efficient Global Optimization of Expensive Black-Box Functions in JOURNAL OF GLOBAL OPTIMIZATION
  • 2005. Scalable Test Problems for Evolutionary Multiobjective Optimization in EVOLUTIONARY MULTIOBJECTIVE OPTIMIZATION
  • 2005-01. A comprehensive survey of fitness approximation in evolutionary computation in SOFT COMPUTING
  • 2005. Surrogate-Assisted Evolutionary Optimization Frameworks for High-Fidelity Engineering Design Problems in KNOWLEDGE INCORPORATION IN EVOLUTIONARY COMPUTATION
  • 2004. Multi-objective Sensor Planning for Efficient and Accurate Object Reconstruction in APPLICATIONS OF EVOLUTIONARY COMPUTING
  • 2008. Meta-Modeling in Multiobjective Optimization in MULTIOBJECTIVE OPTIMIZATION
  • 2004. On Test Functions for Evolutionary Multi-objective Optimization in PARALLEL PROBLEM SOLVING FROM NATURE - PPSN VIII
  • 2006. Multi-objective Optimization with the Naive $$ \mathbb{M} $$ ID $$ \mathbb{E} $$ A in TOWARDS A NEW EVOLUTIONARY COMPUTATION
  • 2003. Is Fitness Inheritance Useful for Real-World Applications? in EVOLUTIONARY MULTI-CRITERION OPTIMIZATION
  • Book

    TITLE

    Evolutionary Multi-Criterion Optimization

    ISBN

    978-3-642-01019-4
    978-3-642-01020-0

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-01020-0_8

    DOI

    http://dx.doi.org/10.1007/978-3-642-01020-0_8

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1042623805


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