avoid back calculation when not needed

Author: jacobdenobel

include/stats.hpp

@@ -16,7 +16,7 @@ namespace parameters
 		Solution global_best = {};
 		bool has_improved = false;
 		Float success_ratio = 2.0 / 11.0;
-		Float cs = 1.0 / 12.0;
+		Float cp = 1.0 / 12.0;
 		size_t last_update = 0;
 		size_t n_updates = 0;
 
@@ -32,7 +32,7 @@ namespace parameters
 
 				has_improved = true;
 			}
-			success_ratio = (1 - cs) * success_ratio + (cs * has_improved);
+			success_ratio = (1 - cp) * success_ratio + (cp * has_improved);
 		}
 	};
 }

src/bounds.cpp

@@ -33,7 +33,7 @@ namespace bounds
 
 	void BoundCorrection::correct(const Eigen::Index i, parameters::Parameters& p)
 	{
-		if (!has_bounds)
+		if (!has_bounds or p.settings.modules.bound_correction == parameters::CorrectionMethod::NONE)
 			return;
 
 		const auto oob = is_out_of_bounds(p.pop.X.col(i));

src/main.cpp

@@ -130,11 +130,11 @@ int main()
 {
 	auto ssa = parameters::StepSizeAdaptation::CSA;
 
-	//run_modcma(parameters::MatrixAdaptationType::NONE, ssa);
-	//run_modcma(parameters::MatrixAdaptationType::SEPERABLE, ssa);
+	run_modcma(parameters::MatrixAdaptationType::NONE, ssa);
+	run_modcma(parameters::MatrixAdaptationType::SEPERABLE, ssa);
 	run_modcma(parameters::MatrixAdaptationType::MATRIX, ssa);
-	//run_modcma(parameters::MatrixAdaptationType::CHOLESKY, ssa);
-	//run_modcma(parameters::MatrixAdaptationType::COVARIANCE, ssa);
-	//run_modcma(parameters::MatrixAdaptationType::COVARIANCE_NO_EIGV, ssa);
+	run_modcma(parameters::MatrixAdaptationType::CHOLESKY, ssa);
+	run_modcma(parameters::MatrixAdaptationType::COVARIANCE, ssa);
+	run_modcma(parameters::MatrixAdaptationType::COVARIANCE_NO_EIGV, ssa);
 	run_modcma(parameters::MatrixAdaptationType::NATURAL_GRADIENT, ssa);
 }

src/matrix_adaptation.cpp

@@ -208,6 +208,8 @@ namespace matrix_adaptation
 	void MatrixAdaptation::adapt_evolution_paths_inner(const Population& pop, const Weights& w,
 		const Stats& stats, const parameters::Settings& settings, const size_t mu, const size_t lambda)
 	{
+		if (settings.one_plus_one && !stats.has_improved)
+			return;
 		ps = (1.0 - w.cs) * ps + (w.sqrt_cs_mueff * dz);
 	}
 
@@ -241,7 +243,7 @@ namespace matrix_adaptation
 			+ (popY * (tau_m * weights).asDiagonal() * popZ.transpose());
 
 
-		if (settings.modules.elitist)
+		if (settings.modules.elitist && !settings.one_plus_one)
 			M_inv = (decay_m * M_inv)
 				+ (tau_1 * ps * (ps.transpose() * M_inv))
 				+ ((popY * (tau_m * weights).asDiagonal()) * (popZ.transpose() * M_inv));

src/selection.cpp

@@ -40,10 +40,14 @@ namespace selection
 		if (p.stats.t != 0)
 		{
 			p.old_pop.resize_cols(k);
-			for (Eigen::Index i = 0; i < static_cast<Eigen::Index>(p.old_pop.n); i++)
+
+			if (!p.settings.one_plus_one)
 			{
-				p.old_pop.Y.col(i).noalias() = p.adaptation->invert_x(p.old_pop.X.col(i), p.old_pop.s(i));
-				p.old_pop.Z.col(i).noalias() = p.adaptation->invert_y(p.old_pop.Y.col(i));
+				for (Eigen::Index i = 0; i < static_cast<Eigen::Index>(p.old_pop.n); i++)
+				{
+					p.old_pop.Y.col(i).noalias() = p.adaptation->invert_x(p.old_pop.X.col(i), p.old_pop.s(i));
+					p.old_pop.Z.col(i).noalias() = p.adaptation->invert_y(p.old_pop.Y.col(i));
+				}
 			}
 			p.pop += p.old_pop;
 		}

src/weights.cpp

@@ -18,7 +18,7 @@ namespace parameters
 			case StepSizeAdaptation::PSR:
 				return 0.9;
 			case StepSizeAdaptation::SR:
-				return 1.0 / 12.0;
+				return 2.0 / (d + 2.0);
 			case StepSizeAdaptation::CSA:
 				return (mueff + 2.0) / (d + mueff + 5.0);
 			default: