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

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...

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Bibliographic Details
Published inMathematics (Basel) Vol. 8; no. 4; p. 513
Main Authors Yoon, Yourim, Kim, Yong-Hyuk
Format Journal Article
LanguageEnglish
Published MDPI AG 01.04.2020
Subjects
genetic algorithm
maximum k-coverage
normalization
redundant representation
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Summary: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.
ISSN:2227-7390
2227-7390
DOI:10.3390/math8040513