Guided genetic algorithm: a new template concept

被引:3
作者
Ben Othman, Hajer [1 ]
Tagina, Moncef [1 ]
机构
[1] Univ Manouba, Natl Sch Comp Sci, COSMOS Lab, Manouba, Tunisia
来源
KNOWLEDGE-BASED AND INTELLIGENT INFORMATION & ENGINEERING SYSTEMS 19TH ANNUAL CONFERENCE, KES-2015 | 2015年 / 60卷
关键词
Maximal constraint satisfaction problems; template concepts; guided genetic algorithm; TABU SEARCH;
D O I
10.1016/j.procs.2015.08.156
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Guided genetic algorithm and dynamic distributed double guided genetic algorithm are based on nature laws and by the Neo-Darwinism theory. These evolutionary approaches were very successful addressing Maximal Constraint Satisfaction Problems (Max-CSPs). Our work is inspired by a little mistake when dealing with in these two algorithms guidance. In fact these approaches are guided by the min-conflict heuristic and the template concept. The used template is distorted. So, we introduce a new template concept in order to allow a better guidance. We suggest considering the percentages of violated constraints in place of their number. This concept is, then, applied to guide the genetic algorithms. In this paper, we compare the latter guided genetic algorithm with our new template guided genetic algorithm. The experimentations show that our new template guidance improves the optimization process to find best solutions in better time. (C) 2015 The Authors. Published by Elsevier B.V.
引用
收藏
页码:1101 / 1108
页数:8
相关论文
共 34 条
[1]  
[Anonymous], 1994, Algorithmes genetiques
[2]  
[Anonymous], 1993, FDN CONSTRAINTS SATI
[3]  
Baker J.E., 1985, Proceedings of the First International Conference on Genetic Algorithms and Their Applications, P101
[4]  
Baker J. E., 1987, P 2 ICGA 87
[5]  
Bellicha A., 1997, REV INTELLIGENCE ART
[6]   A family of distributed double guided genetic algorithm for Max_CSPs [J].
Bouamama, Sadok ;
Ghedira, Khaled .
INTERNATIONAL JOURNAL OF KNOWLEDGE-BASED AND INTELLIGENT ENGINEERING SYSTEMS, 2006, 10 (05) :363-376
[7]  
Bouhmala N., 1995, P 10 INT C APPL ART
[8]  
Briot J.P., 1989, P EUR C OBJ OR PROGR
[9]   A SET PARTITIONING HEURISTIC FOR THE GENERALIZED ASSIGNMENT PROBLEM [J].
CATTRYSSE, DG ;
SALOMON, M ;
VANWASSENHOVE, LN .
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 1994, 72 (01) :167-174
[10]   PARTIAL CONSTRAINT SATISFACTION [J].
FREUDER, EC ;
WALLACE, RJ .
ARTIFICIAL INTELLIGENCE, 1992, 58 (1-3) :21-70