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A Parameter Tuning Framework for Metaheuristics Based on Design of Experiments and Artificial Neural Networks
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Technology and Media.ORCID iD: 0000-0001-9372-3416
2010 (English)In: Proceeding of the International Conference on Computer Mathematics and Natural Computing 2010 / [ed] B. Brojack, WASET , 2010Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, a framework for the simplification andstandardization of metaheuristic related parameter tuning by applyinga four phase methodology, utilizing Design of Experiments andArtificial Neural Networks, is presented. Metaheuristics are multipurposeproblem solvers that are utilized on computational optimizationproblems for which no efficient problem-specific algorithmexists. Their successful application to concrete problems requires thefinding of a good initial parameter setting, which is a tedious andtime-consuming task. Recent research reveals the lack of approachwhen it comes to this so called parameter tuning process. In themajority of publications, researchers do have a weak motivation fortheir respective choices, if any. Because initial parameter settingshave a significant impact on the solutions quality, this course ofaction could lead to suboptimal experimental results, and therebya fraudulent basis for the drawing of conclusions.

Place, publisher, year, edition, pages
WASET , 2010.
Keyword [en]
Parameter Tuning, Metaheuristics, Design of Experiments, Artificial Neural Networks
National Category
Other Computer and Information Science
Identifiers
URN: urn:nbn:se:miun:diva-11420OAI: oai:DiVA.org:miun-11420DiVA, id: diva2:310723
Conference
International Conference on Computer Mathematics and Natural Computing
Available from: 2010-08-02 Created: 2010-04-15 Last updated: 2018-01-12Bibliographically approved
In thesis
1. Automatic Instance-based Tailoring of Parameter Settings for Metaheuristics
Open this publication in new window or tab >>Automatic Instance-based Tailoring of Parameter Settings for Metaheuristics
2011 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Many industrial problems in various fields, such as logistics, process management, orproduct design, can be formalized and expressed as optimization problems in order tomake them solvable by optimization algorithms. However, solvers that guarantee thefinding of optimal solutions (complete) can in practice be unacceptably slow. Thisis one of the reasons why approximative (incomplete) algorithms, producing near-optimal solutions under restrictions (most dominant time), are of vital importance.

Those approximative algorithms go under the umbrella term metaheuristics, each of which is more or less suitable for particular optimization problems. These algorithmsare flexible solvers that only require a representation for solutions and an evaluation function when searching the solution space for optimality.What all metaheuristics have in common is that their search is guided by certain control parameters. These parameters have to be manually set by the user andare generally problem and interdependent: A setting producing near-optimal resultsfor one problem is likely to perform worse for another. Automating the parameter setting process in a sophisticated, computationally cheap, and statistically reliable way is challenging and a significant amount of attention in the artificial intelligence and operational research communities. This activity has not yet produced any major breakthroughs concerning the utilization of problem instance knowledge or the employment of dynamic algorithm configuration.

The thesis promotes automated parameter optimization with reference to the inverse impact of problem instance diversity on the quality of parameter settings with respect to instance-algorithm pairs. It further emphasizes the similarities between static and dynamic algorithm configuration and related problems in order to show how they relate to each other. It further proposes two frameworks for instance-based algorithm configuration and evaluates the experimental results. The first is a recommender system for static configurations, combining experimental design and machine learning. The second framework can be used for static or dynamic configuration,taking advantage of the iterative nature of population-based algorithms, which is a very important sub-class of metaheuristics.

A straightforward implementation of framework one did not result in the expected improvements, supposedly because of pre-stabilization issues. The second approach shows competitive results in the scenario when compared to a state-of-the-art model-free configurator, reducing the training time by in excess of two orders of magnitude.

Place, publisher, year, edition, pages
Östersund: Mid Sweden University, 2011. p. 62
Series
Mid Sweden University licentiate thesis, ISSN 1652-8948 ; 67
Keyword
Algorithm Configuration, Parameter Tuning, Parameter Control, Metaheuristics
National Category
Engineering and Technology
Identifiers
urn:nbn:se:miun:diva-14613 (URN)978-91-86694-48-7 (ISBN)
Presentation
2011-10-14, Q221, Akademigatan 1, Östersund, 22:41 (English)
Opponent
Supervisors
Available from: 2011-10-17 Created: 2011-10-16 Last updated: 2012-08-01Bibliographically approved

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Dobslaw, Felix

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