Improving the Efficiency of Genetic Algorithms for Linearly Constrained Optimization

Show simple item record Vossner, Seigfried en O'Sullivan, Michael en Kausch, Ulrich en 2008-08-20T02:08:48Z en 2008-08-20T02:08:48Z en 2003 en
dc.identifier.citation Report University of Auckland School of Engineering 625, (2003) en
dc.identifier.issn 0111-0136 en
dc.identifier.uri en
dc.description.abstract The efficiency of a Genetic Algorithm for constrained parameter optimization depends heavily on the ratio of feasible to infeasible area in its rectangular search space. We show an algorithm based on existing mathematical programming methods which improves this ratio assuming a set of linear constraints. We approximate the feasible area by a multidimensional ellipsoid and rotate the original search space parallel to its main axes. The minimum volume hyper rectangle we can wrap around the rotated constraint set gives us a new rectangular search space. In addition to that we propose to continue with a local search algorithm for fine tuning. To demonstrate the use of the proposed method, we perform test runs on randomly generated cases as well as on three selected examples. en
dc.language.iso en en
dc.publisher Faculty of Engineering, University of Auckland, New Zealand. en
dc.relation.ispartofseries Report (University of Auckland. Faculty of Engineering) en
dc.relation.isreferencedby UoA1615449 en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. en
dc.rights.uri en
dc.subject.ddc SERIALS Report School Eng en
dc.title Improving the Efficiency of Genetic Algorithms for Linearly Constrained Optimization en
dc.type Technical Report en
dc.subject.marsden Fields of Research::290000 Engineering and Technology en
dc.rights.holder Copyright: the author en
dc.rights.accessrights en Engineering en

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