Solving Temporal Constraint Satisfaction Problems with Heuristic based Evolutionary Algorithms

被引：2
|
作者：
Jashmi, Bahareh Jafari [1 ]
Mouhoub, Malek [1 ]
机构：
[1] Univ Regina, Dept Comp Sci, Regina, SK S4S 0A2, Canada
来源：
20TH IEEE INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE, VOL 2, PROCEEDINGS | 2008年
关键词：
D O I：
10.1109/ICTAI.2008.43
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper we discuss the applicability of evolutionary algorithms enhanced by heuristics and adaptive fitness computation for solving the Temporal Constraint Satisfaction Problem (TCSP). This latter problem is an extension of the well known CSP through our TemPro model, in order to handle numeric and symbolic temporal information. We test the evolutionary algorithms on randomly generated TCSPs and analyze and compare the performance of the algorithms tested, based on different measures. The results show that heuristics do not promise better performance for solving TCSPs. The basic genetic algorithm (GA) and Microgenetic Iterative Descendant (MGID) are the most effective ones. We also noticed that MGID is more efficient than basic GA for easier problems.
引用
收藏
页码：525 / 529
页数：5
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