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A novel genetic algorithm based on the life cycle of dictyostelium

Published: 12 July 2014 Publication History

Abstract

We have proposed the novel evolutionary computation called ``Dictyostelium based Genetic Algorithm" (DGA), which adopts the concept of life cycle of slime molds and can take a balance between exploitation and exploration.
In this research, we propose an extension of DGA with an index evaluating population diversity. To analyze the abilities of DGAs, the computational experiments are carried out taking several combinatorial optimization problems as examples. We show that the performance of DGAs is superior to that of Simple GA in all examples.

References

[1]
Y. Katada, K. Ohkura and K. Ueda: Measuring Neutrality of Fitness Landscapes Based on the Nei's Standard Genetic Distance, Proceedings of 2003 Asia Pacific Symposium on Intelligent and Evolutionary Systems, Technology and Applications, pp.107--114 (2003).
[2]
N. Mori and H. Kita: The entropy evaluation method for the thermodynamical selection rule, In Proceedings of the 5th the Genetic and Evolutionary Computation Conference, pp 799 (1999).

Cited By

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  • (2015)Machine Learning Algorithm for the Fitness Landscape Learning Evolutionary ComputationTransactions of the Institute of Systems, Control and Information Engineers10.5687/iscie.28.18928:5(189-197)Online publication date: 2015
  • (2015)Analyzing exploration exploitation trade-off by means of P-I similarity index and dictyostelium based genetic algorithm2015 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2015.7257202(2548-2555)Online publication date: May-2015

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cover image ACM Conferences
GECCO Comp '14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation
July 2014
1524 pages
ISBN:9781450328814
DOI:10.1145/2598394
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2014

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Author Tags

  1. combinatorial optimization
  2. dictyostelium
  3. fitness landscapes
  4. genetic algorithm
  5. local search

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GECCO '14
Sponsor:
GECCO '14: Genetic and Evolutionary Computation Conference
July 12 - 16, 2014
BC, Vancouver, Canada

Acceptance Rates

GECCO Comp '14 Paper Acceptance Rate 180 of 544 submissions, 33%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

View all
  • (2015)Machine Learning Algorithm for the Fitness Landscape Learning Evolutionary ComputationTransactions of the Institute of Systems, Control and Information Engineers10.5687/iscie.28.18928:5(189-197)Online publication date: 2015
  • (2015)Analyzing exploration exploitation trade-off by means of P-I similarity index and dictyostelium based genetic algorithm2015 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2015.7257202(2548-2555)Online publication date: May-2015

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