By Topic

A cellular automata based genetic algorithm and its application in mechanical design optimisation

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $31
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Cao, Y.J. ; Dept. of Electr. Eng. & Electron., Liverpool Univ., UK ; Wu, Q.H.

Maintaining population diversity is a principal demand for genetic algorithms (GAs) to obtain the global optimum of difficult optimisation problems. The paper presents a cellular automata based genetic algorithm (CAGA). In the CAGA, the individuals in the population are mapped onto a cellular automata to realise the locality and neighbourhood. The mapping is based on the individuals' fitness and the Hamming distances between individuals. The selection of individuals is controlled based on the structure of cellular automata, to avoid the fast population diversity loss and improve the convergence performance during the genetic search. Applications of CAGA to mechanical design optimisation and the coding to cope with different kinds of variables are discussed. The effectiveness of CAGA is demonstrated with two typical mechanical design optimisation problems

Published in:

Control '98. UKACC International Conference on (Conf. Publ. No. 455)

Date of Conference:

1-4 Sep 1998