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Feasibility-preserving crossover for maximum k-coverage problem

Authors:

Yourim Yoon,

Yong-Hyuk Kim,

Byung-Ro MoonAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Pages 593 - 598

https://doi.org/10.1145/1389095.1389209

Published: 12 July 2008 Publication History

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Abstract

The maximum k-coverage problem is a generalized version of covering problems. We introduce the problem formally and analyze its property in relation to the operators of genetic algorithm. Based on the analysis, we propose a new crossover tailored to the maximum k-coverage problem. While traditional n-point crossovers have a problem of requiring repair steps, the proposed crossover has an additional advantage of always producing feasible solutions. We give a comparative analysis of the proposed crossover through experiments.

References

[1]

U. Aickelin. An indirect genetic algorithm for set covering problems. Journal of the Operational Research Society, 53(10):1118--1126, 2002.

Crossref

Google Scholar

[2]

E. Alba, G. Luque, and L. Araujo. Natural language tagging with genetic algorithms. Information Processing Letters, 100(5):173--182, 2006.

Digital Library

Google Scholar

[3]

J. E. Beasley. OR--library: Distributing test problems by electronic mail. Journal of the Operational Research Society, 41:1069--1072, 1990.

Crossref

Google Scholar

[4]

J. E. Beasley and P. C. Chu. A genetic algorithm for the set covering problem. European Journal of Operational Research, 94:392--404, 1996.

Crossref

Google Scholar

[5]

O. Cordon, S. Damasb, and J. Santamaria. Feature-based image registration by means of the CHC evolutionary algorithm. Image and Vision Computing, 24(5):525--533, 2006.

Digital Library

Google Scholar

[6]

L. J. Eshelman. The CHC adaptive search algorithm: how to have safe search when engaging in nontraditional genetic recombination. In Foundations of Genetic Algorithms, pages 265--283. Morgan Kaufmann, 1991.

Google Scholar

[7]

M. Garey and D. S. Johnson. Computers and Intractability: A Guide to the Theory of NP-Completeness. Freeman, San Francisco, 1979.

Digital Library

Google Scholar

[8]

C. Guerra-Salcedo and D. Whitley. Genetic search for feature subset selection: A comparison between CHC and GENESIS. In Proceedings of the Third Annual Conference on Genetic Programming, pages 504--509. Morgan Kaufmann, 1998.

Google Scholar

[9]

D. S. Hochbaum and A. Pathria. Analysis of the greedy approach in problems of maximum k--coverage. Naval Research Logistics, 45(6):615--627, 1998.

Crossref

Google Scholar

[10]

H. W. Kuhn. The Hungarian method for the assignment problem. Naval Research Logistic Quarterly, 2:83--97, 1955.

Crossref

Google Scholar

[11]

A. Moraglio, Y.-H. Kim, Y. Yoon, and B.-R. Moon. Geometric crossovers for multiway graph partitioning. Evolutionary Computation, 15(4):445--474, 2007.

Digital Library

Google Scholar

[12]

A. J. Nebro, E. Alba, G. Molina, F. Chicano, F. Luna, and J. J. Durillo. Optimal antenna placement using a new multi--objective CHC algorithm. In Proceedings of the 9th annual conference on Genetic and evolutionary computation, pages 876--883. ACM, 2007.

Digital Library

Google Scholar

[13]

C. H. Papadimitriou and K.Steiglitz. Combinatorial Optimization: Algorithms and Complexity. Prentice-Hall, 1955.

Digital Library

Google Scholar

[14]

S. Tsutsui and D. E. Goldberg. Search space boundary extension method in real-coded genetic algorithms. Information Sciences, 133(3-4):229--247, 2001.

Digital Library

Google Scholar

Cited By

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Yoon YKim Y(2020)Gene-Similarity Normalization in a Genetic Algorithm for the Maximum k-Coverage ProblemMathematics10.3390/math80405138:4(513)Online publication date: 2-Apr-2020
https://doi.org/10.3390/math8040513
Jung CKim YYoon YMoon BCho YShin SKim SHung CHong J(2014)Investigation of hungarian mating schemes for genetic algorithmsProceedings of the 29th Annual ACM Symposium on Applied Computing10.1145/2554850.2554882(140-147)Online publication date: 24-Mar-2014
https://dl.acm.org/doi/10.1145/2554850.2554882
Yoon YKim YMoraglio AMoon B(2012)Quotient geometric crossovers and redundant encodingsTheoretical Computer Science10.1016/j.tcs.2011.08.015425(4-16)Online publication date: Mar-2012
https://doi.org/10.1016/j.tcs.2011.08.015

Index Terms

Feasibility-preserving crossover for maximum k-coverage problem
1. Applied computing
  1. Physical sciences and engineering
    1. Mathematics and statistics
2. Mathematics of computing
  1. Discrete mathematics
  2. Mathematical software

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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
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

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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Published: 12 July 2008

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GECCO08: Genetic and Evolutionary Computation Conference

July 12 - 16, 2008

GA, Atlanta, USA

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Yoon YKim Y(2020)Gene-Similarity Normalization in a Genetic Algorithm for the Maximum k-Coverage ProblemMathematics10.3390/math80405138:4(513)Online publication date: 2-Apr-2020
https://doi.org/10.3390/math8040513
Jung CKim YYoon YMoon BCho YShin SKim SHung CHong J(2014)Investigation of hungarian mating schemes for genetic algorithmsProceedings of the 29th Annual ACM Symposium on Applied Computing10.1145/2554850.2554882(140-147)Online publication date: 24-Mar-2014
https://dl.acm.org/doi/10.1145/2554850.2554882
Yoon YKim YMoraglio AMoon B(2012)Quotient geometric crossovers and redundant encodingsTheoretical Computer Science10.1016/j.tcs.2011.08.015425(4-16)Online publication date: Mar-2012
https://doi.org/10.1016/j.tcs.2011.08.015

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Neural network crossover in genetic algorithms using genetic programming

Comparison of a crossover operator in binary-coded genetic algorithms

Optimized Crossover-Based Genetic Algorithms for the Maximum Cardinality and Maximum Weight Clique Problems

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