A New Genetic Algorithm for Set Covering Problems

An indirect genetic algorithm for the non-unicost set covering problem is presented. The algorithm is a two-stage meta-heuristic, which in the past was successfully applied to similar multiple-choice optimisation problems. The two stages of the algorithm are an ‘indirect’ genetic algorithm and a dec...

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Main Author: Aickelin, Uwe
Format: Conference or Workshop Item
Published: 2000
Online Access:https://eprints.nottingham.ac.uk/606/
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author Aickelin, Uwe
author_facet Aickelin, Uwe
author_sort Aickelin, Uwe
building Nottingham Research Data Repository
collection Online Access
description An indirect genetic algorithm for the non-unicost set covering problem is presented. The algorithm is a two-stage meta-heuristic, which in the past was successfully applied to similar multiple-choice optimisation problems. The two stages of the algorithm are an ‘indirect’ genetic algorithm and a decoder routine. First, the solutions to the problem are encoded as permutations of the rows to be covered, which are subsequently ordered by the genetic algorithm. Fitness assignment is handled by the decoder, which transforms the permutations into actual solutions to the set covering problem. This is done by exploiting both problem structure and problem specific information. However, flexibility is retained by a self-adjusting element within the decoder, which allows adjustments to both the data and to stages within the search process. Computational results are presented.
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format Conference or Workshop Item
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spelling nottingham-6062020-05-04T20:32:52Z https://eprints.nottingham.ac.uk/606/ A New Genetic Algorithm for Set Covering Problems Aickelin, Uwe An indirect genetic algorithm for the non-unicost set covering problem is presented. The algorithm is a two-stage meta-heuristic, which in the past was successfully applied to similar multiple-choice optimisation problems. The two stages of the algorithm are an ‘indirect’ genetic algorithm and a decoder routine. First, the solutions to the problem are encoded as permutations of the rows to be covered, which are subsequently ordered by the genetic algorithm. Fitness assignment is handled by the decoder, which transforms the permutations into actual solutions to the set covering problem. This is done by exploiting both problem structure and problem specific information. However, flexibility is retained by a self-adjusting element within the decoder, which allows adjustments to both the data and to stages within the search process. Computational results are presented. 2000 Conference or Workshop Item PeerReviewed Aickelin, Uwe (2000) A New Genetic Algorithm for Set Covering Problems. In: Annual Operational Research Conference 42 (OR 42), Swansea, UK.
spellingShingle Aickelin, Uwe
A New Genetic Algorithm for Set Covering Problems
title A New Genetic Algorithm for Set Covering Problems
title_full A New Genetic Algorithm for Set Covering Problems
title_fullStr A New Genetic Algorithm for Set Covering Problems
title_full_unstemmed A New Genetic Algorithm for Set Covering Problems
title_short A New Genetic Algorithm for Set Covering Problems
title_sort new genetic algorithm for set covering problems
url https://eprints.nottingham.ac.uk/606/