Skip to Main Content
ASME Press Select Proceedings

International Conference on Computer Technology and Development, 3rd (ICCTD 2011)

Jianhong Zhou
Jianhong Zhou
Search for other works by this author on:
No. of Pages:
ASME Press
Publication date:

The SGA (Simple Genetic Algorithm) depends on many factors and the two most important ones are Crossover Probability and Mutation Probability. In a successful run of SGA, the average fitness of the population after mating is better than the initial average fitness i.e. fnew>finit Where fnew is average fitness after mating and finit is average fitness before mating. The average fitness is defined as the average of fitness of chromosomes in the mating pool. The chromosomes are implemented as bit strings (i.e. 1001). These bit strings are represented using integers.The performance of SGA is evaluated in this paper by varying the crossover probability and mutation probability. The results are compared with the usual SGA.

This content is only available via PDF.
Close Modal
This Feature Is Available To Subscribers Only

Sign In or Create an Account

Close Modal
Close Modal