Bioinspired computation in combinatorial optimization: algorithms and their computational complexity

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedings – Annual report year: 2012Researchpeer-review

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Bioinspired computation methods, such as evolutionary algorithms and ant colony optimization, are being applied successfully to complex engineering and combinatorial optimization problems, and it is very important that we understand the computational complexity of these algorithms. This tutorials explains the most important results achieved in this area.

The presenters show how runtime behavior can be analyzed in a rigorous way, in particular for combinatorial optimization. They present well-known problems such as minimum spanning trees, shortest paths, maximum matching, and covering and scheduling problems. Classical single objective optimization is examined first. They then investigate the computational complexity of bioinspired computation applied to multiobjective variants of the considered combinatorial optimization problems, and in particular they show how multiobjective optimization can help to speed up bioinspired computation for single-objective optimization problems.

The tutorial is based on a book written by the authors with the same title. Further information about the book can be found at
Original languageEnglish
Title of host publicationProceedings of the fourteenth international conference on Genetic and evolutionary computation : Companion
PublisherAssociation for Computing Machinery
Publication date2012
ISBN (Print)978-1-4503-1178-6
Publication statusPublished - 2012
EventGenetic and Evolutionary Computation Conference (GECCO 2012) - Philadelphia, United States
Duration: 7 Jul 201211 Jul 2012


ConferenceGenetic and Evolutionary Computation Conference (GECCO 2012)
CountryUnited States
Internet address
CitationsWeb of Science® Times Cited: No match on DOI

ID: 33440632