Evolutionary Algorithms - How to Cope With Plateaus of Constant Fitness and When to Reject Strings of the Same Fitness

dc.contributor.authorJansen, Thomasde
dc.contributor.authorWegener, Ingode
dc.date.accessioned2004-12-07T08:20:35Z
dc.date.available2004-12-07T08:20:35Z
dc.date.created2000de
dc.date.issued2001-10-17de
dc.description.abstractThe most simple evolutionary algorithm,the so-called (1+1)EA accepts a child if its fitness is at least as large (in the case of maximization) as the fittness of its parent. The variant (1 +1)*EA only accepts a child if its fitness is strictly larger than the fitness of its parent. Here two functions related to the class of long path functions are presented such that the (1 +1)EA maximizes one of it i polynomial time and needs exponential time for the other while the (1+1)*EA has the opposite behavior. These results prove that small changes of a evolutionary algorithm may change its behavior significantly. Since the (1 +1)EA and the (1 +1)*EA differ only o plateaus of constant fitness, the results also show how evolutionary algorithms behave on such plateaus. The (1 +1)EA can pass a path of constant fitness and polynomial length in polynomial time. Finally, for these functions it is shown that local performance measures like the quality gain and the progress rate do not describe the global behavior of evolutionary algorithms.en
dc.format.extent202328 bytes
dc.format.extent935233 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/postscript
dc.identifier.urihttp://hdl.handle.net/2003/5398
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-15253
dc.language.isoende
dc.publisherUniversität Dortmundde
dc.relation.ispartofseriesReihe Computational Intelligence ; 96de
dc.subject.ddc530de
dc.titleEvolutionary Algorithms - How to Cope With Plateaus of Constant Fitness and When to Reject Strings of the Same Fitnessen
dc.typeTextde
dc.type.publicationtypereport
dcterms.accessRightsopen access

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