A comparative study of three evolutionary algorithms incorporating different amounts of domain knowledge for node covering problem

被引:29
作者
He, J [1 ]
Yao, X [1 ]
Li, J [1 ]
机构
[1] Univ Birmingham, Sch Comp Sci, Ctr Excellence Res Computat Intelligence & Applic, Birmingham B15 2TT, W Midlands, England
来源
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS | 2005年 / 35卷 / 02期
基金
英国工程与自然科学研究理事会;
关键词
algorithm design; heuristic knowledge; optimization methods; performance analysis;
D O I
10.1109/TSMCC.2004.841903
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper compares three different evolutionary algorithms for solving the node covering problem: EA-I relies on the definition of the problem only without using any domain knowledge, while EA-II and EA-III employ extra heuristic knowledge. In theory, it is proven that all three algorithms can find an optimal solution in finite generations and find a feasible solution efficiently; but none of them can find the optimal solution efficiently for all instances of the problem. Through experiments, it is observed that all three algorithms can find a feasible solution efficiently, and the algorithms with extra heuristic knowledge can find better approximation solutions, but none of them can find the optimal solution to the first instance efficiently. This paper shows that heuristic knowledge is helpful for evolutionary algorithms to find good approximation solutions, but it contributes little to search for the optimal solution in some instances.
引用
收藏
页码:266 / 271
页数:6
相关论文
共 11 条
[1]   How to analyse evolutionary algorithms [J].
Beyer, HG ;
Schwefel, HP ;
Wegener, I .
THEORETICAL COMPUTER SCIENCE, 2002, 287 (01) :101-130
[2]  
Droste S, 1999, GECCO-99: PROCEEDINGS OF THE GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, P833
[3]  
English T. M., 1996, Evolutionary Programming V. Proceedings of the Fifth Annual Conference on Evolutionary Programming, P163
[4]  
Evans I., 1998, P EP 98, P377
[5]   Towards an analytic framework for analysing the computation time of evolutionary algorithms [J].
He, J ;
Yao, X .
ARTIFICIAL INTELLIGENCE, 2003, 145 (1-2) :59-97
[6]   Drift analysis and average time complexity of evolutionary algorithms [J].
He, J ;
Yao, X .
ARTIFICIAL INTELLIGENCE, 2001, 127 (01) :57-85
[7]  
Khuri S., 1994, GENETIC ALGORITHMS F, P86
[8]  
MOTWANI R, 1992, CSTR921435 STANF U D
[9]  
Papadimitriou C., 1998, COMBINATORIAL OPTIMI
[10]  
Schumacher C., 2001, Proceedings of the Genetic and Evolutionary Computation Conference GECCO-2001, P565