stochastic ranking for constrained evolutionary optimization - tec311r

8/20/2019 Stochastic Ranking for Constrained Evolutionary Optimization - Tec311r

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St oc hast ic Rank ing f or Const r ainedEvol ut ionar y Opt imizat ion

Thomas Philip Runarsson and Xin Yao

Abstract

K eywor ds

I . Int r oduct ion

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Aver age per cent age f easibl e individual s in t he f inal popul at ion as a f unct ion of ( ) andt est f unct ion pr esent ed in A ppendix A .

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I I I. Ex per iment al St udies

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Exper iment al r esul t s on t hir t een benchmar k f unct ions using ES wit h st ochast ic r anking ( ).independent r uns wer e car r ied out .

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Compar ison bet ween our (indic at ed by RY ) and K oziel and M ichal ewic z' s (indicat ed by K M [14])al gor it hms. For pr obl em , t he r esul t was t aken f r om M et hod # 4 in [15]. T he t wo val ues in t he

`M ean' c ol umn f or pr obl em r epr esent medians.

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Exper iment al r esul t s on t hir t een benchmar k f unct ions using ES wit h st ochast ic r anking ( ).independent r uns wer e car r ied out .

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Ex per iment al r esul t s on t hir t een benchmar k f unct ions using ES wit h st ochast ic r anking( ) . i ndepend ent r uns w er e c ar r ied o ut . ) Based on t he f easibl e sol ut ions f ound out

o f t he r uns.

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Exper iment al r esul t on f unct ion using ES wit h st ochast ic r anking and É .

Exper iment al r esul t s using t he dynamic penal t y met hod of [12] wit h . T he subscr ipt in t hef unct ion name indicat es t he number of f easibl e sol ut ions f ound if it was bet ween and incl usive.

\ { " means no f easibl e sol ut ions wer e f ound.

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Ex per iment al r esul t s using t he dyn amic penal t y met hod of [12] w it h 2. \ { " means no

f easibl e sol ut ion s w er e f oun d.

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IV . Concl usion

Acknowl edgement


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Ref er ences


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Appendix

I. T est Funct ion Suit e

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stochastic ranking for constrained evolutionary optimization - tec311r

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