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Iteration-wise parameter learning
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Technology and Media.ORCID iD: 0000-0001-9372-3416
2011 (English)In: 2011 IEEE Congress of Evolutionary Computation, CEC 2011, New Orleans, LA: IEEE conference proceedings, 2011, 455-462 p.Conference paper, Published paper (Refereed)
Abstract [en]

Adjusting the control parameters of population-based algorithms is a means for improving the quality of these algorithms' result when solving optimization problems. The difficulty lies in determining when to assign individual values to specific parameters during the run. This paper investigates the possible implications of a generic and computationally cheap approach towards parameter analysis for population-based algorithms. The effect of parameter settings was analyzed in the application of a genetic algorithm to a set of traveling salesman problem instances. The findings suggest that statistics about local changes of a search from iteration i to iteration i + 1 can provide valuable insight into the sensitivity of the algorithm to parameter values. A simple method for choosing static parameter settings has been shown to recommend settings competitive to those extracted from a state-of-the-art parameter tuner, paramlLS, with major time and setup advantages.

Place, publisher, year, edition, pages
New Orleans, LA: IEEE conference proceedings, 2011. 455-462 p.
Keyword [en]
Algorithm Configuration, Parameter Tuning, Metaheuristics
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:miun:diva-14612DOI: 10.1109/CEC.2011.5949653ISI: 000312932600063Scopus ID: 2-s2.0-80052003971ISBN: 978-1-4244-7834-7 (print)OAI: oai:DiVA.org:miun-14612DiVA: diva2:448375
Conference
2011 IEEE Congress of Evolutionary Computation, CEC 2011;New Orleans, LA;5 June 2011through8 June 2011;Code86068
Note

2011 IEEE Congress of Evolutionary Computation, CEC 2011; New Orleans, LA; 5 June 2011 through 8 June 2011; Code 86068

Available from: 2011-10-16 Created: 2011-10-16 Last updated: 2014-08-31Bibliographically 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. 62 p.
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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Citation style
  • apa
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More styles
Language
  • de-DE
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Output format
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  • asciidoc
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