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Biased random-key genetic algorithm for linearly-constrained global optimization

Published: 06 July 2013 Publication History

Abstract

In this paper, we propose a biased random key genetic algorithm for finding approximate solutions for bound-constrained continuous global optimization problems subject to linear constraints. Experimental results illustrate its effectiveness on the g01 and g14 problems from CEC2006 benchmark [5].

References

[1]
R. Aiex, M. Resende, and C. Ribeiro. Prob. distribution of solution time in GRASP: An experimental investigation. J. of Heuristics, 8:343--373, 2002.
[2]
M. Ericsson, M. Resende, and P. Pardalos. A genetic algorithm for the weight setting prob. in OSPF routing. J. of Combinatorial Optimization, 6:299--333, 2002.
[3]
C. Floudas and P. Pardalos. Collection of test probs. for constrained global optimization algs. Springer-Verlag New York, Inc., New York, NY, USA, 1990.
[4]
D. Himmelblau. Applied nonlinear programming. McGraw-Hill, 1972.
[5]
J. J. Liang, T. P. Runarsson, E. M. Montes, M. Clerc, P. N. Suganthan, C. A. Coello, and D. K. Problem Definitions and Evaluation Criteria for the CEC 2006 Special Session on Constrained Real-Parameter Optimization. Technical report, 2006.
[6]
W. Spears and K. DeJong. On the virtues of parameterized uniform crossover. In Proceedings of the Fourth International Conference on Genetic Algorithms, pages 230--236, 1991.

Cited By

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  • (2018)Solving Capacitated Closed Vehicle Routing Problem with Time Windows (CCVRPTW) using BRKGA with local searchIOP Conference Series: Materials Science and Engineering10.1088/1757-899X/352/1/012014352(012014)Online publication date: 11-May-2018
  • (2015)Survey on applications of biased-random key genetic algorithms for solving optimization problems2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)10.1109/IEEM.2015.7385771(863-870)Online publication date: Dec-2015

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Published In

cover image ACM Conferences
GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
July 2013
1798 pages
ISBN:9781450319645
DOI:10.1145/2464576
  • Editor:
  • Christian Blum,
  • General Chair:
  • Enrique Alba
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: 06 July 2013

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Author Tags

  1. biased random key genetic algorithm
  2. continuous optimization
  3. global optimization
  4. heuristic
  5. linear constraints

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Conference

GECCO '13
Sponsor:
GECCO '13: Genetic and Evolutionary Computation Conference
July 6 - 10, 2013
Amsterdam, The Netherlands

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

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Cited By

View all
  • (2018)Solving Capacitated Closed Vehicle Routing Problem with Time Windows (CCVRPTW) using BRKGA with local searchIOP Conference Series: Materials Science and Engineering10.1088/1757-899X/352/1/012014352(012014)Online publication date: 11-May-2018
  • (2015)Survey on applications of biased-random key genetic algorithms for solving optimization problems2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)10.1109/IEEM.2015.7385771(863-870)Online publication date: Dec-2015

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