Fitness is used to determine which chromosomes will be used for the next generation. Mathematically, for chromosome Sk: fitness = f(Sk) where: k = 1,..., n; n is a population size, which function that you use to calculate the fitness is obviously dependant on how you decide to encode the string to represent your problem.
References:
Genetic Algorithms and Evolutionary Computation (April 23, 2004), Adam Marczyk, http://www.talkorigins.org/faqs/genalg/genalg.html
Book Reference: David Edward Goldberg (1989), “Genetic Algorithms in Search, Optimization and Machine Learning” Addison-Wesley.
Related Posts:
Table of Contents
© 2006 Kumaravel & Project Team
If references link found broken see the below e-printed version of webpage
Note: e-printed version of the webpage, are just for the reference and it was not owned by blog author. It had be created using the Open Source PDFCreator, which is environment friendly to save paper.
References:
Genetic Algorithms and Evolutionary Computation (April 23, 2004), Adam Marczyk, http://www.talkorigins.org/faqs/genalg/genalg.html
Book Reference: David Edward Goldberg (1989), “Genetic Algorithms in Search, Optimization and Machine Learning” Addison-Wesley.
Related Posts:
Table of Contents
© 2006 Kumaravel & Project Team
If references link found broken see the below e-printed version of webpage
Note: e-printed version of the webpage, are just for the reference and it was not owned by blog author. It had be created using the Open Source PDFCreator, which is environment friendly to save paper.
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