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// Scilab ( http://www.scilab.org/ ) - This file is part of Scilab
// Copyright (C) DIGITEO - Yann COLLETTE
// Copyright (C) 2012 - DIGITEO - Allan CORNET
//
// This file is released under the 3-clause BSD license. See COPYING-BSD.
/////////////////////////////////////////////////////////////////
// example of use of the genetic algorithm on a 2D Ising model //
/////////////////////////////////////////////////////////////////
function demo_ising()
// Loading test problems
path = get_absolute_file_path("GAIsing2ddemo.sce");
getd(path + "/Ising");
// Load the crossover and mutation operators for the Ising 2D problem
getd(path + "/.");
J = 1.1;
H = 0.7;
Magnet = "%T";
Connect = "%T";
clear f;
// For the maximization case, we must have a checker board solution (+1 -1 +1 -1 ....)
deff("y=f(x)","y = - ising2d(x,"+string(J)+","+string(H)+","+Magnet+","+Connect+")");
PopSize = 100;
Proba_cross = 0.7;
Proba_mut = 0.1;
NbGen = 10;
NbCouples = 110;
Log = %T;
nb_disp = 10; // Nb point to display from the optimal population
pressure = 0.05;
DisplayIndiv = %T;
ga_params = init_param();
// Parameters to adapt to the shape of the optimization problem
ga_params = add_param(ga_params, "dimension", 10);
ga_params = add_param(ga_params, "proba", 0.05);
// Parameters to fine tune the Genetic algorithm. All these parameters are optional for continuous optimization
// If you need to adapt the GA to a special problem, you
ga_params = add_param(ga_params, "init_func", init_ga_ising2d);
ga_params = add_param(ga_params, "crossover_func", crossover_ga_ising2d);
ga_params = add_param(ga_params, "mutation_func", mutation_ga_ising2d);
ga_params = add_param(ga_params, "codage_func", coding_ga_identity);
ga_params = add_param(ga_params, "selection_func", selection_ga_elitist);
//ga_params = add_param(ga_params, 'selection_func', selection_ga_random);
ga_params = add_param(ga_params, "nb_couples", NbCouples);
ga_params = add_param(ga_params, "pressure", pressure);
///////////////////////
// Genetic Algorithm //
///////////////////////
printf("\n");
printf(gettext("%s: optimization starting, please wait ...\n"),"GAIsing");
[pop_opt, fobj_pop_opt, pop_init, fobj_pop_init] = optim_ga(f, PopSize, NbGen, Proba_mut, Proba_cross, Log, ga_params);
printf("Genetic Algorithm: %d points from pop_opt\n", nb_disp);
for i = 1 : nb_disp
printf("Individual %d: f = %f\n", i, fobj_pop_opt(i));
if DisplayIndiv then disp(pop_opt(i)); end
end
my_handle = scf(100001);
clf(my_handle,"reset");
plot_ising2d(pop_opt(1));
demo_viewCode("GAIsing2ddemo.sce");
endfunction
demo_ising();
clear demo_ising;
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