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121227s2004 gw | s |||| 0|eng d |
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|a 9783540248552
|9 978-3-540-24855-2
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|a 10.1007/b98645
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|a TJ210.2-211.495
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|a Genetic and Evolutionary Computation – GECCO 2004
|h [electronic resource] :
|b Genetic and Evolutionary Computation Conference, Seattle, WA, USA, June 26-30, 2004. Proceedings, Part II /
|c edited by Kalyanmoy Deb.
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|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg,
|c 2004.
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|a C, 1448 p. 660 illus.
|b online resource.
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|a text
|b txt
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|a computer
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|a Lecture Notes in Computer Science,
|x 0302-9743 ;
|v 3103
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|a Genetic Algorithms (Continued) -- Genetic Algorithms – Posters -- Genetic Programming -- Genetic Programming – Posters -- Learning Classifier Systems -- Learning Classifier Systems – Poster -- Real World Applications -- Real World Applications – Posters -- Search-Based Software Engineering -- Search-Based Software Engineering – Posters.
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|a MostMOEAsuseadistancemetricorothercrowdingmethodinobjectivespaceinorder to maintain diversity for the non-dominated solutions on the Pareto optimal front. By ensuring diversity among the non-dominated solutions, it is possible to choose from a variety of solutions when attempting to solve a speci?c problem at hand. Supposewehavetwoobjectivefunctionsf (x)andf (x).Inthiscasewecande?ne 1 2 thedistancemetricastheEuclideandistanceinobjectivespacebetweentwoneighboring individuals and we thus obtain a distance given by 2 2 2 d (x ,x )=[f (x )?f (x )] +[f (x )?f (x )] . (1) 1 2 1 1 1 2 2 1 2 2 f wherex andx are two distinct individuals that are neighboring in objective space. If 1 2 2 2 the functions are badly scaled, e.g.[?f (x)] [?f (x)] , the distance metric can be 1 2 approximated to 2 2 d (x ,x )? [f (x )?f (x )] . (2) 1 2 1 1 1 2 f Insomecasesthisapproximationwillresultinanacceptablespreadofsolutionsalong the Pareto front, especially for small gradual slope changes as shown in the illustrated example in Fig. 1. 1.0 0.8 0.6 0.4 0.2 0 0 20 40 60 80 100 f 1 Fig.1.Forfrontswithsmallgradualslopechangesanacceptabledistributioncanbeobtainedeven if one of the objectives (in this casef ) is neglected from the distance calculations. 2 As can be seen in the ?gure, the distances marked by the arrows are not equal, but the solutions can still be seen to cover the front relatively well.
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|a Computer science.
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|a Microprocessors.
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|a Computers.
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|a Algorithms.
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|a Computer science
|x Mathematics.
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|a Artificial intelligence.
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|a Computer Science.
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|a Artificial Intelligence (incl. Robotics).
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|a Computer Science, general.
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|a Computation by Abstract Devices.
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|a Algorithm Analysis and Problem Complexity.
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|a Processor Architectures.
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|a Discrete Mathematics in Computer Science.
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|a Deb, Kalyanmoy.
|e editor.
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|a SpringerLink (Online service)
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|t Springer eBooks
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|i Printed edition:
|z 9783540223436
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|a Lecture Notes in Computer Science,
|x 0302-9743 ;
|v 3103
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|u http://dx.doi.org/10.1007/b98645
|z Full Text via HEAL-Link
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|a ZDB-2-SCS
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|a ZDB-2-LNC
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|a Computer Science (Springer-11645)
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