Genetic and Evolutionary Computation – GECCO 2004 Genetic and Evolutionary Computation Conference, Seattle, WA, USA, June 26-30, 2004. Proceedings, Part II /

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...

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Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Άλλοι συγγραφείς: Deb, Kalyanmoy (Επιμελητής έκδοσης)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2004.
Σειρά:Lecture Notes in Computer Science, 3103
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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245 1 0 |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. 
264 1 |a Berlin, Heidelberg :  |b Springer Berlin Heidelberg,  |c 2004. 
300 |a C, 1448 p. 660 illus.  |b online resource. 
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490 1 |a Lecture Notes in Computer Science,  |x 0302-9743 ;  |v 3103 
505 0 |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. 
520 |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. 
650 0 |a Computer science. 
650 0 |a Microprocessors. 
650 0 |a Computers. 
650 0 |a Algorithms. 
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650 2 4 |a Computer Science, general. 
650 2 4 |a Computation by Abstract Devices. 
650 2 4 |a Algorithm Analysis and Problem Complexity. 
650 2 4 |a Processor Architectures. 
650 2 4 |a Discrete Mathematics in Computer Science. 
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