Genetic algorithms in computer aided design

被引:360
作者
Renner, G
Ekárt, A
机构
[1] Hungarian Acad Sci, Comp & Automat Res Inst, H-1518 Budapest, Hungary
[2] Univ Birmingham, Sch Comp Sci, Birmingham B15 2TT, W Midlands, England
关键词
CAD; genetic algorithms; optimization; geometric design; conceptual design; mechanism design;
D O I
10.1016/S0010-4485(03)00003-4
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Design is a complex engineering activity, in which computers are more and more involved. The design task can often be seen as an optimization problem in which the parameters or the structure describing the best quality design are sought. Genetic algorithms constitute a class of search algorithms especially suited to solving complex optimization problems. In addition to parameter optimization, genetic algorithms are also suggested for solving problems in creative design, such as combining components in a novel, creative way. Genetic algorithms transpose the notions of evolution in Nature to computers and imitate natural evolution. Basically, they find solution(s) to a problem by maintaining a population of possible solutions according to the 'survival of the fittest' principle. We present here the main features of genetic algorithms and several ways in which they can solve difficult design problems. We briefly introduce the basic notions of genetic algorithms, namely, representation, genetic operators, fitness evaluation, and selection. We discuss several advanced genetic algorithms that have proved to be efficient in solving difficult design problems. We then give an overview of applications of genetic algorithms to different domains of engineering design. (C) 2003 Elsevier Science Ltd. All rights reserved.
引用
收藏
页码:709 / 726
页数:18
相关论文
共 98 条
[1]   CONCURRENT GENETIC ALGORITHMS FOR OPTIMIZATION OF LARGE STRUCTURE [J].
ADELI, H ;
CHENG, NT .
JOURNAL OF AEROSPACE ENGINEERING, 1994, 7 (03) :276-296
[2]  
Adeli H., 1993, Journal of Aerospace Engineering, V6, P315, DOI DOI 10.1061/(ASCE)0893-1321(1993)6:4(315)
[3]  
[Anonymous], INTRO EVOLUTIONARY D
[4]  
Asimov M., 1962, INTRO DESIGN
[5]  
BACK TB, 1997, IEEE T EVOLUTIONARY, P1
[6]   A voxel-based representation for evolutionary shape optimization [J].
Baron, P ;
Fisher, R ;
Tuson, A ;
Mill, F ;
Sherlock, A .
AI EDAM-ARTIFICIAL INTELLIGENCE FOR ENGINEERING DESIGN ANALYSIS AND MANUFACTURING, 1999, 13 (03) :145-156
[7]  
BEASLEY D, 1993, U COMPUT, V15, P58
[8]   A Sequential Niche Technique for Multimodal Function Optimization [J].
Beasley, David ;
Bull, David R. ;
Martin, Ralph R. .
EVOLUTIONARY COMPUTATION, 1993, 1 (02) :101-125
[9]  
Bentley P. J., 1997, Engineering Design & Automation, V3, P119
[10]  
BENTLEY PJ, 2002, INTRO CREATIVE EVOLU, P1