In metaheuristic algorithms applied to certain problems, it may be difficult to design search operators that guarantee producing feasible search points. In such cases, it may be more efficient to allow a search operator to yield an infeasible solution, and then turn it into a feasible one using a repair process. This paper is an attempt to provide a broad perspective on the candidate solution repair and frame it as a metaheuristic design pattern.
@INPROCEEDINGS { Krawiec:2014:GECCOWorkshop,
ABSTRACT = { In metaheuristic algorithms applied to certain problems, it may be difficult to design search operators that guarantee producing feasible search points. In such cases, it may be more efficient to allow a search operator to yield an infeasible solution, and then turn it into a feasible one using a repair process. This paper is an attempt to provide a broad perspective on the candidate solution repair and frame it as a metaheuristic design pattern. },
ACMID = { 2609847 },
ADDRESS = { New York, NY, USA },
AUTHOR = { Krawiec, Krzysztof },
BOOKTITLE = { Proceedings of the 2014 Conference Companion on Genetic and Evolutionary Computation Companion },
DOI = { 10.1145/2598394.2609847 },
ISBN = { 978-1-4503-2881-4 },
KEYWORDS = { feasibility, metaheuristic algorithms, search operators, solution repair },
LOCATION = { Vancouver, BC, Canada },
NUMPAGES = { 4 },
PAGES = { 1415--1418 },
PUBLISHER = { ACM },
SERIES = { GECCO Comp '14 },
TITLE = { Metaheuristic Design Pattern: Candidate Solution Repair },
URL = { http://doi.acm.org/10.1145/2598394.2609847 },
YEAR = { 2014 },
1 = { http://doi.acm.org/10.1145/2598394.2609847 },
2 = { https://doi.org/10.1145/2598394.2609847 },
}