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Informative performance metrics for dynamic optimisation problems

Published: 07 July 2007 Publication History

Abstract

Existing metrics for dynamic optimisation are designed primarily to rate an algorithm's overall performance. These metrics show whether one algorithm is better than another, but do not indicate any specific aspects of the performance. In this paper we split the offline error metric into two component parts. We propose a new metric to measure convergence speed, and show how this, when combined with a population diversity metric, correlates strongly with the overall performance.
We then use these metrics to analyse several optimisation algorithms, yielding new insight into both the test function and how the algorithms' characteristics can be improved.

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Cited By

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  • (2013)Dynamic Constrained Optimization with offspring repair based Gravitational Search Algorithm2013 IEEE Congress on Evolutionary Computation10.1109/CEC.2013.6557858(2414-2421)Online publication date: Jun-2013
  • (2013)Evolutionary Dynamic Optimization: Test and Evaluation EnvironmentsEvolutionary Computation for Dynamic Optimization Problems10.1007/978-3-642-38416-5_1(3-37)Online publication date: 2013
  • (2013)Quantitative Performance Measures for Dynamic Optimization ProblemsMetaheuristics for Dynamic Optimization10.1007/978-3-642-30665-5_2(17-33)Online publication date: 2013
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cover image ACM Conferences
GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
July 2007
2313 pages
ISBN:9781595936974
DOI:10.1145/1276958
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2007

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Author Tags

  1. dynamic optimisation
  2. evolutionary computation
  3. multimodal function optimisation
  4. particle swarms

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GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

View all
  • (2013)Dynamic Constrained Optimization with offspring repair based Gravitational Search Algorithm2013 IEEE Congress on Evolutionary Computation10.1109/CEC.2013.6557858(2414-2421)Online publication date: Jun-2013
  • (2013)Evolutionary Dynamic Optimization: Test and Evaluation EnvironmentsEvolutionary Computation for Dynamic Optimization Problems10.1007/978-3-642-38416-5_1(3-37)Online publication date: 2013
  • (2013)Quantitative Performance Measures for Dynamic Optimization ProblemsMetaheuristics for Dynamic Optimization10.1007/978-3-642-30665-5_2(17-33)Online publication date: 2013
  • (2013)Differential Evolution and Offspring Repair Method Based Dynamic Constrained Optimization4th International Conference on Swarm, Evolutionary, and Memetic Computing - Volume 829710.1007/978-3-319-03753-0_27(298-309)Online publication date: 19-Dec-2013
  • (2010)ABC, a new performance tool for algorithms solving dynamic optimization problemsIEEE Congress on Evolutionary Computation10.1109/CEC.2010.5586406(1-7)Online publication date: Jul-2010
  • (2010)Measuring fitness degradation in dynamic optimization problemsProceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I10.1007/978-3-642-12239-2_59(572-581)Online publication date: 7-Apr-2010
  • (2010)Improving Local Convergence in Particle Swarms by Fitness Approximation Using RegressionComputational Intelligence in Expensive Optimization Problems10.1007/978-3-642-10701-6_11(265-293)Online publication date: 2010
  • (2009)Simple control rules in a cooperative system for dynamic optimisation problemsInternational Journal of General Systems10.1080/0308107080236736638:7(701-717)Online publication date: Oct-2009
  • (2007)Using regression to improve local convergence2007 IEEE Congress on Evolutionary Computation10.1109/CEC.2007.4424524(592-599)Online publication date: Sep-2007

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