A robust genetic algorithm for rectangle packing problem
Chen De-Sheng (),
Chang-Tzu Lin () and
Yi-Wen Wang ()
Additional contact information
Chen De-Sheng: Feng Chia University
Chang-Tzu Lin: Feng Chia University
Yi-Wen Wang: Feng Chia University
Journal of Combinatorial Optimization, 2007, vol. 14, issue 4, No 10, 500-500
Abstract:
Abstract Genetic algorithms (GAs) have been found well suited for solving optimization problems. This paper introduces a robust genetic algorithm that efficiently solves the classical rectangle packing problem. Based on an encoding scheme of sliceable structure, the proposed algorithm develops a new crossover operation that inherits favorable fitness properties from its parents without further repairing. Empirical results verify two characteristics of this algorithm. First, it can achieve very promising runtime and high-quality solutions. Second, it can manage a much larger problem size compared to the state-of-the-art algorithms.
Keywords: Genetic algorithm; Rectangle packing; VLSI (search for similar items in EconPapers)
Date: 2007
References: View complete reference list from CitEc
Citations:
Downloads: (external link)
http://link.springer.com/10.1007/s10878-006-8463-4 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:jcomop:v:14:y:2007:i:4:d:10.1007_s10878-006-8463-4
Ordering information: This journal article can be ordered from
https://www.springer.com/journal/10878
DOI: 10.1007/s10878-006-8463-4
Access Statistics for this article
Journal of Combinatorial Optimization is currently edited by Thai, My T.
More articles in Journal of Combinatorial Optimization from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().