An implemention of AineGA With Evolutionary Multiobjective optimization, no parallel yet based on the work of Silva et.al. "Parallel Niche Pareto AlinegaGA - An evolutionary multiobjective approach on Multiple Sequence Alignment" Journal of Integrative Bioinformatics, 8(3): 174, 2011
License: GNU General Public License v3.0
Python 100.00%
nalineaga's Introduction
NAlineaGA
A gentic algorithm for multiple sequence alignment witten in python.
Based on
da Silva, F. J. M.J., Pérez, J. M. S.M., Pulido, J. A. G.A., and Rodríguez, M. V.A. (2011). Parallel niche pareto AlineaGA-an evolutionary multiobjective approach on multiple sequence alignment. Journal of integrative bioinformatics, 8(3).
needs improving of the niche pareto part
sigma_share the value that determines the size of the niche around
every indivual is hard codec to 3.14 there is a branch with a function
calc_sigma_share which implements the methode used in the above paper
but did not give good results. Every individul was in every niche
difites the puropuse of a niching system.
I think it may be because there is such a big differenct between the
sum of pairs and identity sop could be 1000+ and identity never gose above 12
Also the large range of sop may cause problems -2000 +1000.
thats in the pareto branch.
The file GA.py is the starting point
Create a GA object wiht the path to a .rsf file as the parameter
my_GA = GA.GA('mypath/myfile.rsf')
And run
my_GA.run()
Alot of the parameters are controled from GA.py
Crossover.py has some adjustible parmaters you can change
As with Mutate.py
David Morrisroe
pizzadave108 at gmail.com
2014-13-03