--- a/inst/__ga_problem__.m
+++ b/inst/__ga_problem__.m
@@ -26,7 +26,7 @@
                    "randnstate", randn ("state"));
 
   ## instructions not to be executed at each generation
-  state.Population(1:problem.options.PopulationSize, 1:problem.nvars) = \
+  state.Population(1:problem.options.PopulationSize, 1:problem.nvars) = ...
       __ga_initial_population__ (problem.nvars,
                                  problem.fitnessfcn,
                                  problem.options);
@@ -45,20 +45,20 @@
     ## elite
     if (private_state.ReproductionCount.elite > 0)
       [trash IndexSortedScores] = sort (state.Score);
-      NextPopulation(state.Selection.elite, 1:problem.nvars) = \
-          state.Population \
+      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__ \
+    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__ \
+      NextPopulation(state.Selection.crossover, 1:problem.nvars) = ...
+          __ga_crossoverfcn__ ...
           (parents(1, private_state.parentsSelection.crossover),
            problem.options, problem.nvars, problem.fitnessfcn,
            false, ## unused
@@ -67,8 +67,8 @@
 
     ## mutation
     if (private_state.ReproductionCount.mutation > 0)
-      NextPopulation(state.Selection.mutation, 1:problem.nvars) = \
-          __ga_mutationfcn__ \
+      NextPopulation(state.Selection.mutation, 1:problem.nvars) = ...
+          __ga_mutationfcn__ ...
           (parents(1, private_state.parentsSelection.mutation),
            problem.options, problem.nvars, problem.fitnessfcn,
            state, state.Score,
@@ -83,7 +83,7 @@
                                                             private_state);
   endwhile
 
-  [x fval exitflag output population scores] = \
+  [x fval exitflag output population scores] = ...
       __ga_problem_return_variables__ (state, problem);
 endfunction