fix_concurrency issue

README.md

@@ -149,9 +149,7 @@ inputs = (x1,x2,u1,u2,u3)
         size(a_pred[1]) != size(a[1]) && return (typemax(Float64),)
 
         loss = norm(a_pred .- a)
-        return (loss,)
-    else
-        return (elem.fitness,)
+        elem.fitness = (loss,)
     end
 end
 fit!(regressor, epochs, population_size, loss_new)

examples/Main_min_example_mo.jl

@@ -26,12 +26,10 @@ regressor = GepRegressor(number_features; number_of_objectives=2)
     try
         if isnan(mean(elem.fitness)) || validate
             y_pred = elem.compiled_function(x_data', regressor.operators_)
-            return (get_loss_function("mse")(y_data, y_pred), length(elem.expression_raw)*0.01)
-        else
-            return elem.fitness
+            elem.fitness = (get_loss_function("mse")(y_data, y_pred), length(elem.expression_raw)*0.01)
         end
     catch e
-        return (typemax(Float64),typemax(Float64))
+        elem.fitness = (typemax(Float64),typemax(Float64))
     end
 end
 

paper/ConstraintViaSBP.jl

@@ -89,7 +89,7 @@ function main()
                 @show ("Current case: ", case_name)
                 #gep_params
                 epochs = 1000
-                population_size = 200
+                population_size = 500
 
                 results = DataFrame(Seed=[],
                     Name=String[], NoiseLeve=String[], Fitness=Float64[], Equation=String[], R2_test=Float64[],
@@ -129,21 +129,21 @@ function main()
                     try
                         if isnan(mean(elem.fitness)) || validate
                             y_pred = elem.compiled_function(x_train', regressor.operators_)
-                            return (get_loss_function("mse")(y_train, y_pred),)
+                            elem.fitness = (get_loss_function("mse")(y_train, y_pred),)
                         else
-                            return (elem.fitness, length(elem.expression_raw) * elem.fitness)
+                            elem.fitness = (elem.fitness, length(elem.expression_raw) * elem.fitness)
                             #return (elem.fitness,)
                         end
                     catch e
-                        return (typemax(Float64),typemax(Float64))
+                        elem.fitness = (typemax(Float64),typemax(Float64))
                     end
                 end
 
 
                 #perform the regression by entering epochs, population_size, the feature cols, the target col and the loss function
                 fit!(regressor, epochs, population_size, x_train', y_train;
                     x_test=x_test', y_test=y_test', target_dimension=target_dim,
-                    loss_fun="mse", break_condition=break_condition)
+                    loss_fun="mse", break_condition=break_condition, correction_amount=0.5)
 
                 end_time = (time_ns() - start_time) / 1e9
                 elem = regressor.best_models_[1]

src/Gep.jl

@@ -118,12 +118,10 @@ Returns the computed fitness value (loss) or crash_value if computation fails
     try
         if isnan(mean(elem.fitness)) || validate
             y_pred = elem.compiled_function(evalArgs.x_data, evalArgs.operators)
-            return (evalArgs.loss_function(evalArgs.y_data, y_pred),)
-        else
-            return elem.fitness
+            elem.fitness = (evalArgs.loss_function(evalArgs.y_data, y_pred),)
         end
     catch e
-        return (evalArgs.crash_value,)
+        elem.fitness = (evalArgs.crash_value,)
     end
 end
 
@@ -342,16 +340,15 @@ The evolution process stops when either:
         Threads.@threads for i in eachindex(population[1:population_size])
             if isnan(mean(population[i].fitness))
                 key = join(population[i].expression_raw, ",")
-                cache_value = get(fit_cache, key, nothing)
-
-                if isnothing(cache_value)
-                    population[i].fitness = compute_fitness(population[i], evalStrategy)
+                cache_value = key in keys(fit_cache)
+                if !(cache_value)
+                    compute_fitness(population[i], evalStrategy)
                     lock(cache_lock)
-                    fit_cache[key] = population[i].fitness
+                        fit_cache[key] = population[i].fitness
                     unlock(cache_lock)
                 else
                     atomic_add!(same, 1)
-                    population[i].fitness = cache_value
+                    population[i].fitness = toolbox.fitness_reset[1]
                 end
             end
         end

tutorial/JGEP_demo.ipynb

@@ -294,7 +294,7 @@
     "#define the \n",
     "regressor = GepRegressor(number_features)\n",
     "\n",
-    "fit!(regressor, epochs, population_size, x_data, y_data; loss_fun=\"mse\")\n",
+    "fit!(regressor, epochs, population_size, x_data', y_data; loss_fun=\"mse\")\n",
     "\n",
     "pred = regressor(x_data')\n",
     "\n",