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

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1 Citation (Scopus)

Abstract

The tutorial demonstrates how to apply and analyze metaheuristics using HeuristicLab, an open source optimization environment. It will be 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 will learn how to assemble different algorithms and parameter settings to a large scale optimization experiment and how to execute such experiments on multi-core or cluster systems. Furthermore, the experiment results will be compared using HeuristicLab's interactive charts for visual and statistical analysis to gain knowledge from the executed test runs. To complete the tutorial, it will be 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.

Original languageEnglish
Title of host publicationGenetic and Evolutionary Computation Conference, GECCO'11 - Companion Publication
Pages1411-1438
Number of pages28
DOIs
Publication statusPublished - 2011
Event13th Annual Genetic and Evolutionary Computation Conference, GECCO'11 - Dublin, Ireland
Duration: 12 Jul 201116 Jul 2011

Publication series

NameGenetic and Evolutionary Computation Conference, GECCO'11 - Companion Publication

Conference

Conference13th Annual Genetic and Evolutionary Computation Conference, GECCO'11
CountryIreland
CityDublin
Period12.07.201116.07.2011

Keywords

  • algorithm analysis
  • algorithm modeling
  • algorithm test
  • heuristic optimization software systems
  • heuristiclab

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