Modeling Permutations for Genetic Algorithms

被引：7
|
作者：
Kroemer, Pavel [1 ]
Platos, Jan [1 ]
Snasel, Vaclav [1 ]
机构：
[1] Tech Univ Ostrava, Dept Comp Sci, VSB, Ostrava 70833, Czech Republic
来源：
2009 INTERNATIONAL CONFERENCE OF SOFT COMPUTING AND PATTERN RECOGNITION | 2009年
关键词：
LINEAR ORDERING PROBLEM; SEARCH;
D O I：
10.1109/SoCPaR.2009.31
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Combinatorial optimization problems form a class of appealing theoretical and practical problems attractive for their complexity and known hardness. They are often NP-hard and as such not solvable by exact methods. Combinatorial optimization problems are subject to numerous heuristic and metaheuristic algorithms, including genetic algorithms. In this paper, we present two new permutation encodings for genetic algorithms and experimentally evaluate the influence of the encodings on the performance and result of genetic algorithm on two synthetic and real-world optimization problems.
引用
收藏
页码：100 / 105
页数：6
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