simpleGA + deriving classes
Vieri Di Paola
vieridipaola at yahoo.com
Thu Oct 26 13:55:45 EDT 2000
I'm a beginner in GA programming and downloaded GAlib
recently. I was wondering if someone could help me
with the following problem.
The algorithm is SGA. I have a population of 400
chromosomes and I use GA1Dbinarystrings of length 32,
max generations is user definable. I need to define my
objective function which analyzes the bit values of
the chromosomes and compares them with
separately-defined proteins (1Dbinarystrings). Should
I do the following?
float
Objective(GAGenome& g){
GA1DBinaryString& genome = (GA1DBinaryString &)g;
// here I compare protein bit values and chromosome
bit values
return *the result*;
}
(No decoding must be performed)
I would also like the program to consider, regardless
of user input, a mutation probability of 0.1 up to
generation 50 and a probability of 0.0 for successive
generations. How can I do that in a simple way?
Do I need to define a crossover() function in order
to:
1 - pick a random number from 1 to 3
2 - if I get 1 then cross site is 8
if I get 2 then cross site is 16
if I get 3 then cross site is 24
(it's a single point crossover)?
I hope I've been clear enough. Let me put it another
way. I am adapting the SGA of David E. Goldberg's book
"Genetic Algorithms in Search, Optimization & Machine
Learning" to GAlib with the above mentioned
variations.
Thank you in advance for your help,
Vieri Di Paola
=====
Vieri Di Paola
dipaola at cli.di.unipi.it
vieridipaola at yahoo.com
University of Pisa
Italy
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