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Open AccessArticle

Gene-Similarity Normalization in a Genetic Algorithm for the Maximum k-Coverage Problem

by Yourim Yoon 1 and Yong-Hyuk Kim 2,*
1
Department of Computer Engineering, College of Information Technology, Gachon University, 1342 Seongnamdaero, Sujeong-gu, Seongnam-si, Gyeonggi-do 13120, Korea
2
School of Software, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, Korea
*
Author to whom correspondence should be addressed.
Mathematics 2020, 8(4), 513; https://doi.org/10.3390/math8040513
Received: 28 February 2020 / Revised: 27 March 2020 / Accepted: 30 March 2020 / Published: 2 April 2020
(This article belongs to the Special Issue Evolutionary Computation & Swarm Intelligence)
The maximum k-coverage problem (MKCP) is a generalized covering problem which can be solved by genetic algorithms, but their operation is impeded by redundancy in the representation of solutions to MKCP. We introduce a normalization step for candidate solutions based on distance between genes which ensures that a standard crossover such as uniform and n-point crossovers produces a feasible solution and improves the solution quality. We present results from experiments in which this normalization was applied to a single crossover operation, and also results for example MKCPs. View Full-Text
Keywords: maximum k-coverage; redundant representation; normalization; genetic algorithm maximum k-coverage; redundant representation; normalization; genetic algorithm
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Yoon, Y.; Kim, Y.-H. Gene-Similarity Normalization in a Genetic Algorithm for the Maximum k-Coverage Problem. Mathematics 2020, 8, 513.

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