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Parameter tuned CMA-ES on the CEC'15 expensive problems
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och automatiseringsteknik, Production and Automation Engineering)
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0001-5436-2128
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0003-0111-1776
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0003-3973-3394
2015 (English)In: Evolutionary Computation, IEEE conference proceedings, 2015, p. 1950-1957Conference paper, Published paper (Refereed)
Abstract [en]

Evolutionary optimization algorithms have parameters that are used to adapt the search strategy to suit different optimization problems. Selecting the optimal parameter values for a given problem is difficult without a-priori knowledge. Experimental studies can provide this knowledge by finding the best parameter values for a specific set of problems. This knowledge can also be constructed into heuristics (rule-of-thumbs) that can adapt the parameters for the problem. The aim of this paper is to assess the heuristics of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) optimization algorithm. This is accomplished by tuning CMA-ES parameters so as to maximize its performance on the CEC'15 problems, using a bilevel optimization approach that searches for the optimal parameter values. The optimized parameter values are compared against the parameter values suggested by the heuristics. The difference between specialized and generalized parameter values are also investigated.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2015. p. 1950-1957
Keywords [en]
Parameter tuning, CMA-ES
National Category
Computer and Information Sciences
Research subject
Technology; Production and Automation Engineering
Identifiers
URN: urn:nbn:se:his:diva-11599DOI: 10.1109/CEC.2015.7257124ISI: 000380444801129Scopus ID: 2-s2.0-84963626635ISBN: 978-1-4799-7492-4 (print)OAI: oai:DiVA.org:his-11599DiVA, id: diva2:860215
Conference
2015 IEEE Congress on Evolutionary Computation (CEC)
Available from: 2015-10-12 Created: 2015-10-12 Last updated: 2018-11-07Bibliographically approved
In thesis
1. A bilevel approach to parameter tuning of optimization algorithms using evolutionary computing: Understanding optimization algorithms through optimization
Open this publication in new window or tab >>A bilevel approach to parameter tuning of optimization algorithms using evolutionary computing: Understanding optimization algorithms through optimization
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Most optimization problems found in the real world cannot be solved using analytical methods. For these types of difficult optimization problems, an alternative approach is needed. Metaheuristics are a category of optimization algorithms that do not guarantee that an optimal solution will be found, but instead search for the best solutions using some general heuristics. Metaheuristics have been shown to be effective at finding “good-enough” solutions to a wide variety of difficult problems. Most metaheuristics involve control parameters that can be used to modify how the heuristics perform its search. This is necessary because different problems may require different search strategies to be solved effectively. The control parameters allow for the optimization algorithm to be adapted to the problem at hand. It is, however, difficult to predict what the optimal control parameters are for any given problem. The problem of finding these optimal control parameter values is known as parameter tuning and is the main topic of this thesis. This thesis uses a bilevel optimization approach to solve parameter tuning problems. In this approach, the parameter tuning problem itself is formulated as an optimization problem and solved with an optimization algorithm. The parameter tuning problem formulated as a bilevel optimization problem is challenging because of nonlinear objective functions, interacting variables, multiple local optima, and noise. However, it is in precisely this kind of difficult optimization problem that evolutionary algorithms, which are a subclass of metaheuristics, have been shown to be effective. That is the motivation for using evolutionary algorithms for the upper-level optimization (i.e. tuning algorithm) of the bilevel optimization approach. Solving the parameter tuning problem using a bilevel optimization approach is also computationally expensive, since a complete optimization run has to be completed for every evaluation of a set of control parameter values. It is therefore important that the tuning algorithm be as efficient as possible, so that the parameter tuning problem can be solved to a satisfactory level with relatively few evaluations. Even so, bilevel optimization experiments can take a long time to run on a single computer. There is, however, considerable parallelization potential in the bilevel optimization approach, since many of the optimizations are independent of one another. This thesis has three primary aims: first, to present a bilevel optimization framework and software architecture for parallel parameter tuning; second, to use this framework and software architecture to evaluate and configure evolutionary algorithms as tuners and compare them with other parameter tuning methods; and, finally, to use parameter tuning experiments to gain new insights into and understanding of how optimization algorithms work and how they can used be to their maximum potential. The proposed framework and software architecture have been implemented and deployed in more than one hundred computers running many thousands of parameter tuning experiments for many millions of optimizations. This illustrates that this design and implementation approach can handle large parameter tuning experiments. Two types of evolutionary algorithms, i.e. differential evolution (DE) and a genetic algorithm (GA), have been evaluated as tuners against the parameter tuning algorithm irace. The as pects of algorithm configuration and noise handling for DE and the GA as related to the parameter tuning problem were also investigated. The results indicate that dynamic resampling strategies outperform static resampling strategies. It was also shown that the GA needs an explicit exploration and exploitation strategy in order not become stuck in local optima. The comparison with irace shows that both DE and the GA can significantly outperform it in a variety of different tuning problems.

Place, publisher, year, edition, pages
Skövde: University of Skövde, 2018. p. 210
Series
Dissertation Series ; 25
National Category
Information Systems, Social aspects
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-16368 (URN)978-91-984187-7-4 (ISBN)
Public defence
2018-09-24, ASSAR Industrial Innovation Arena, Skövde, 10:00
Opponent
Supervisors
Available from: 2018-11-15 Created: 2018-11-07 Last updated: 2018-11-15Bibliographically approved

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Andersson, MartinBandaru, SunithNg, Amos H. C.Syberfeldt, Anna

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