Algorithm and experiment design with HeuristicLab: An open source optimization environment for research and education

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

2 Citations (Scopus)

Abstract

This tutorial demonstrates how to apply and analyze metaheuristic optimization algorithms using the HeuristicLab open source optimization environment. It is shown how to parameterize and execute evolutionary algorithms to solve combinatorial optimization problems (traveling salesman, vehicle routing) as well as data analysis problems (regression, classification). The attendees learn how to assemble different algorithms and parameter settings to large scale optimization experiments and how to execute such experiments on multi-core or cluster systems. Furthermore, the experiment results are compared using HeuristicLab's interactive charts for visual and statistical analysis to gain knowledge from the executed test runs. To complete the tutorial, it is sketched briefly how HeuristicLab can be extended with further optimization problems and how custom optimization algorithms can be modeled using the graphical algorithm designer. Additional details on HeuristicLab can be found at http://dev.heuristiclab.com. Copyright is held by the author/owner(s).

Original languageEnglish
Title of host publicationGECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation Companion
Pages1287-1316
Number of pages30
DOIs
Publication statusPublished - 2012
Event14th International Conference on Genetic and Evolutionary Computation, GECCO'12 - Philadelphia, PA, United States
Duration: 7 Jul 201211 Jul 2012

Publication series

NameGECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation Companion

Conference

Conference14th International Conference on Genetic and Evolutionary Computation, GECCO'12
CountryUnited States
CityPhiladelphia, PA
Period07.07.201211.07.2012

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