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Voronoi-initializated island models for solving real-coded deceptive problems

Published: 12 July 2008 Publication History

Abstract

Deceptive problems have always been considered difficult for Genetic Algorithms. To cope with this characteristic, the literature has proposed the use of Parallel Genetic Algorithms (PGAs), particularly multi-population island-based models. Although the existence of multiple populations encourages population diversity, these problems are still difficult to solve. This paper introduces a new initialization mechanism for each of the populations of the islands based on Voronoi cells. In order to analyze the results, a series of different experiments using several real-value deceptive problems and a set of representative parameters (migration ratio, migration frequency and connectivity) have been chosen. The results obtained suggest that the Voronoi initialization method improves considerably the performance obtained with a traditionally uniform random initialization.

References

[1]
F. Aurenhammer. Voronoi diagrams, a survey of a fundamental geometric data structure. ACM Comput. Surv., 23:345--405, 1991.
[2]
T. C. Belding. The distributed genetic algorithm revisited. In Proceedings of the Sixth ICGA, pages 114--121. Morgan Kaufmann, 1995.
[3]
H. Bersini, M. Dorigo, S. Langerman, G. Seront, and L. Gambardella. Results of the first international contest on evolutionary optimisation (1st iceo). In ICEC, pages 611--615, 1996.
[4]
H. Beyer and K. Deb. On self-adaptive features in real-parameter evolutionary algorithms. IEEE TEC, 5(3):250--270, 2001.
[5]
E. Cantú-Paz. Efficient and accurate parallel genetic algorithms. Kluwer Academic Publishers, 2001.
[6]
D. Dasgupta. Handling deceptive problems using a different genetic search. In ICEC, pages 807--811, 1994.
[7]
H. de Garis. Genetic programming: artificial nervous systems artificial embryos and embryological electronics. In PPSN - Proceedings of 1st Workshop, volume 496, pages 117--123. Springer-Verlag, Berlin, Germany, 1-3 1991.
[8]
D. Goldberg. Genetic Algorithms and Simulated Annealing, chapter Simple Genetic Algorithms and the Minimal Deceptive Problem, pages 74--88. Morgan Kaufmann, 1987.
[9]
D. E. Goldberg. Real-coded genetic algorithms, virtual alphabets, and blocking. Complex Systems, 5:139--167, 1991.
[10]
D. E. Goldberg, M. Pelikan, and S. Tsutsui. Getting the best of both worlds: Discrete and continuous genetic and evolutionary algorithms in concert. Information Sciences, 156(3-4):147--171, 2003.
[11]
J. Grefensette. Genetic Algorithms and Simulated Annealing, chapter Incorporating problem specific knowledge into genetic algorithms, pages 42--46. Morgan Kauffmann Publishers, 1987.
[12]
F. Herrera, M. Lozano, and A. Sánchez. A taxonomy for the crossover operator for real-coded genetic algorithms. an experimental study. International Journal of Intelligent Systems, 18(3):309--338, 2003.
[13]
J. Holland. Cohort GAs and hyperplane-defined functions. Evolutionary Computation, 8(4):372--391, 2000.
[14]
L. Kallel and M. Schoenauer. Alternative random initialization in genetic algorithms. In Proceedings of the 7th ICGA, pages 268--275, 1997.
[15]
J. R. Koza and D. Andre. Parallel genetic programming on a network of transputers. Technical report, 1995.
[16]
J.-P. Li, M. E. Balazs, G. T. Parks, and J. P. Clarkson. A species conserving genetic algorithm for multimodal function optimization. Evol. Comput., 10(3):207--234, 2002.
[17]
K. Ohkura and K. Ueda. An extended framework for overcoming premature convergence. In Proceedings of the 7th ICGA, pages 260--267, 1997.
[18]
H. Pei and E. Goodman. A comparison of cohort genetic algorithms with canonical serial and island-model distributed GA's. In GECCO'01, pages 501--510, 2001.
[19]
C. Petty and M. Leuze. A theoretical investigation of a parallel genetic algorithm. In Proc. 3rd ICGA, pages 398--405, 1989.
[20]
M. Saska, M. Hess, and K. Schilling. Voronoi strains: a spline path planning algorithm for complex environments. In Proceedings of the 25th IASTED International Multi-Conference, pages 498--502, 2007. ACTA Press.
[21]
I. Sekaj. Robust parallel genetic algorithms with re-initialisation. In PPSN, pages 411--419, 2004.
[22]
Z. Skolicki and K. De Jong. The influence of migration sizes and intervals on island models. In GECCO '05, pages 1295--1302. ACM Press, 2005.
[23]
Z. Skolicki and K. A. D. Jong. Improving evolutionary algorithms with multi-representation island models. In PPSN, pages 420--429, 2004.
[24]
P. A. Whigham and G. Dick. A voronoi-based distributed genetic algorithm. In Fifteenth Annual Colloquium of the Spatial Information Research Centre, 2003.
[25]
D. Whitley, S. Rana, and R. Heckendorn. The island model genetic algorithm: On separability, population size and convergence. Journal of Computing and Information Technology, 7:33--47, 1999.
[26]
L. D. Whitley. Fundamental principles of deception in genetic search. In Foundations of genetic algorithms, pages 221--241. Morgan Kaufmann, 1991.
[27]
D. Wyatt and H. Lipson. Finding building blocks through eigenstructure adaptation. In GECCO'03, page 207, 2003.

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  • (2012)Parallel metaheuristics: recent advances and new trendsInternational Transactions in Operational Research10.1111/j.1475-3995.2012.00862.x20:1(1-48)Online publication date: 13-Aug-2012

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    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
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    Published: 12 July 2008

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    Author Tags

    1. deceptive problems
    2. migration topology
    3. parallel genetic algorithms
    4. voronoi initialization

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    • (2025)Location, Size, and CapacityInto a Deeper Understanding of Evolutionary Computing: Exploration, Exploitation, and Parameter Control10.1007/978-3-031-75577-4_1(1-152)Online publication date: 18-Jan-2025
    • (2012)Parallel metaheuristics: recent advances and new trendsInternational Transactions in Operational Research10.1111/j.1475-3995.2012.00862.x20:1(1-48)Online publication date: 13-Aug-2012

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