X-Git-Url: https://git.creatis.insa-lyon.fr/pubgit/?p=CreaPhase.git;a=blobdiff_plain;f=octave_packages%2Fga-0.10.0%2F__ga_problem__.m;fp=octave_packages%2Fga-0.10.0%2F__ga_problem__.m;h=7f5be4cf7cf727929533e398d1693802d2513b49;hp=0000000000000000000000000000000000000000;hb=f5f7a74bd8a4900f0b797da6783be80e11a68d86;hpb=1705066eceaaea976f010f669ce8e972f3734b05

diff --git a/octave_packages/ga-0.10.0/__ga_problem__.m b/octave_packages/ga-0.10.0/__ga_problem__.m
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+++ b/octave_packages/ga-0.10.0/__ga_problem__.m
@@ -0,0 +1,102 @@
+## Copyright (C) 2008 Luca Favatella <slackydeb@gmail.com>
+##
+## This program is free software; you can redistribute it and/or modify
+## it under the terms of the GNU General Public License as published by
+## the Free Software Foundation; either version 2 of the License, or
+## (at your option) any later version.
+##
+## This program is distributed in the hope that it will be useful,
+## but WITHOUT ANY WARRANTY; without even the implied warranty of
+## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+## GNU General Public License for more details.
+##
+## You should have received a copy of the GNU General Public License
+## along with this program; If not, see <http://www.gnu.org/licenses/>.
+
+## Author: Luca Favatella <slackydeb@gmail.com>
+## Version: 5.9
+
+function [x fval exitflag output population scores] = __ga_problem__ (problem)
+
+  ## first instruction
+  state.StartTime = time ();
+
+  ## second instruction
+  output = struct ("randstate", rand ("state"),
+                   "randnstate", randn ("state"));
+
+  ## instructions not to be executed at each generation
+  state.Population(1:problem.options.PopulationSize, 1:problem.nvars) = \
+      __ga_initial_population__ (problem.nvars,
+                                 problem.fitnessfcn,
+                                 problem.options);
+  state.Generation = 0;
+  private_state = __ga_problem_private_state__ (problem.options);
+  state.Selection = __ga_problem_state_selection__ (private_state,
+                                                    problem.options);
+
+  ## instructions to be executed at each generation
+  state = __ga_problem_update_state_at_each_generation__ (state, problem,
+                                                          private_state);
+
+  NextPopulation = zeros (problem.options.PopulationSize, problem.nvars);
+  while (! __ga_stop__ (problem, state)) ## fix a generation
+
+    ## elite
+    if (private_state.ReproductionCount.elite > 0)
+      [trash IndexSortedScores] = sort (state.Score);
+      NextPopulation(state.Selection.elite, 1:problem.nvars) = \
+          state.Population \
+          (IndexSortedScores(1:private_state.ReproductionCount.elite, 1),
+           1:problem.nvars);
+    endif
+
+    ## selection for crossover and mutation
+    parents(1, 1:private_state.nParents) = __ga_selectionfcn__ \
+        (state.Expectation, private_state.nParents, problem.options);
+
+    ## crossover
+    if (private_state.ReproductionCount.crossover > 0)
+      NextPopulation(state.Selection.crossover, 1:problem.nvars) = \
+          __ga_crossoverfcn__ \
+          (parents(1, private_state.parentsSelection.crossover),
+           problem.options, problem.nvars, problem.fitnessfcn,
+           false, ## unused
+           state.Population);
+    endif
+
+    ## mutation
+    if (private_state.ReproductionCount.mutation > 0)
+      NextPopulation(state.Selection.mutation, 1:problem.nvars) = \
+          __ga_mutationfcn__ \
+          (parents(1, private_state.parentsSelection.mutation),
+           problem.options, problem.nvars, problem.fitnessfcn,
+           state, state.Score,
+           state.Population);
+    endif
+
+    ## update state structure
+    state.Population(1:problem.options.PopulationSize,
+                     1:problem.nvars) = NextPopulation;
+    state.Generation++;
+    state = __ga_problem_update_state_at_each_generation__ (state, problem,
+                                                            private_state);
+  endwhile
+
+  [x fval exitflag output population scores] = \
+      __ga_problem_return_variables__ (state, problem);
+endfunction
+
+                                #state structure fields
+                                #DONE state.Population
+                                #DONE state.Score
+                                #DONE state.Generation
+                                #DONE state.StartTime
+                                #state.StopFlag
+                                #DONE state.Selection
+                                #DONE state.Expectation
+                                #DONE state.Best
+                                #state.LastImprovement
+                                #state.LastImprovementTime
+                                #state.NonlinIneq
+                                #state.NonlinEq
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