Restart-Based Genetic Algorithm for the Quadratic Assignment Problem

被引：1
|
作者：
Misevicius, Alfonsas [1 ]
机构：
[1] Kaunas Univ Technol, LT-51368 Kaunas, Lithuania
来源：
RESEARCH AND DEVELOPMENT IN INTELLIGENT SYSTEMS XXV | 2009年
关键词：
LOCAL SEARCH;
D O I：
10.1007/978-1-84882-171-2_7
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The power of genetic algorithms (GAs) has been demonstrated for various domains of the computer science, including combinatorial optimization. In this paper, we propose a new conceptual modification of the genetic algorithm entitled a "restart-based genetic algorithm" (RGA). An effective implementation of RGA for a well-known combinatorial optimization problem, the quadratic assignment problem (QAP), is discussed. The results obtained from the computational experiments on the QAP instances from the publicly available library QAPLIB show excellent performance of RGA. This is especially true for the real-life like QAPs.
引用
收藏
页码：91 / 104
页数：14
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