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Solving discrete deceptive problems with EMMRS

Published: 12 July 2008 Publication History

Abstract

This paper presents a new method for solving discrete deceptive problems using a genotype to phenotype mapping where a new replacement and shift operator is applied. The method is evaluated using different deceptive problems. Experimental results show how our method obtains a speed-up of 94% with respect to other approaches.

References

[1]
R. Chow. 2004. Evolving Genotype to Phenotype Mappings with a Multiple-Chromosome Genetic Algorithm. In GECCO 2004, LNCS 3102, 1006--101 Springer.
[2]
P. Schwefel. 1981. Numerical Optimization of Computer Models. John Wiley & Sons. English translation of Numerische Optimierung von Computer-Modellen mittels der Evolutionsstrategie, 1977.
[3]
A. Törn and A. Zilinskas. 1989. Global Optimization. Lecture Notes in Computer Science, N° 350, Springer-Verlag, Berlin.

Cited By

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  • (2014)Solving GA-hard problems with EMMRS and GPGPUsProceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation10.1145/2576768.2598219(1007-1014)Online publication date: 12-Jul-2014
  • (2012)Analysis of a triploid genetic algorithm over deceptive and epistatic landscapesACM SIGAPP Applied Computing Review10.1145/2387358.238736212:3(51-59)Online publication date: 1-Sep-2012
  • (2012)Analysis of a triploid genetic algorithm over deceptive landscapesProceedings of the 27th Annual ACM Symposium on Applied Computing10.1145/2245276.2245324(244-249)Online publication date: 26-Mar-2012
  • Show More Cited By

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Published In

cover image ACM Conferences
GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
July 2008
1814 pages
ISBN:9781605581309
DOI:10.1145/1389095
  • Conference Chair:
  • Conor Ryan,
  • Editor:
  • Maarten Keijzer
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2008

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Author Tags

  1. deceptive problems
  2. genetic algorithms
  3. genotype and phenotype mapping

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GECCO08
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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

View all
  • (2014)Solving GA-hard problems with EMMRS and GPGPUsProceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation10.1145/2576768.2598219(1007-1014)Online publication date: 12-Jul-2014
  • (2012)Analysis of a triploid genetic algorithm over deceptive and epistatic landscapesACM SIGAPP Applied Computing Review10.1145/2387358.238736212:3(51-59)Online publication date: 1-Sep-2012
  • (2012)Analysis of a triploid genetic algorithm over deceptive landscapesProceedings of the 27th Annual ACM Symposium on Applied Computing10.1145/2245276.2245324(244-249)Online publication date: 26-Mar-2012
  • (2011)An analysis of multi-chromosome GAs in deceptive problemsProceedings of the 13th annual conference on Genetic and evolutionary computation10.1145/2001576.2001715(1021-1028)Online publication date: 12-Jul-2011

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