Debugging genetic search

src/apex/apex_kokkos_tuning.cpp

@@ -227,6 +227,9 @@ class KokkosSession {
             } else if (strncmp(apex::apex_options::kokkos_tuning_policy(),
                     "simulated_annealing", strlen("simulated_annealing")) == 0) {
                 strategy = apex_ah_tuning_strategy::SIMULATED_ANNEALING;
+            } else if (strncmp(apex::apex_options::kokkos_tuning_policy(),
+                    "genetic_search", strlen("genetic_search")) == 0) {
+                strategy = apex_ah_tuning_strategy::GENETIC_SEARCH;
             } else {
                 strategy = apex_ah_tuning_strategy::NELDER_MEAD;
             }

src/apex/apex_policies.cpp

@@ -1817,7 +1817,7 @@ inline int __common_setup_custom_tuning(shared_ptr<apex_tuning_session>
             }
             );
         }
-    } else if (request.strategy == apex_ah_tuning_strategy::APEX_GENETIC) {
+    } else if (request.strategy == apex_ah_tuning_strategy::GENETIC_SEARCH) {
         status = __genetic_setup(tuning_session, request);
         if(status == APEX_NOERROR) {
             apex::register_policy(

src/apex/apex_policies.hpp

@@ -41,7 +41,7 @@ enum class apex_ah_tuning_strategy : int {
     EXHAUSTIVE, RANDOM, NELDER_MEAD,
     PARALLEL_RANK_ORDER, SIMULATED_ANNEALING,
     APEX_EXHAUSTIVE, APEX_RANDOM,
-    APEX_GENETIC};
+    GENETIC_SEARCH};
 
 struct apex_tuning_session;
 class apex_tuning_request;

src/apex/genetic_search.cpp

@@ -87,10 +87,15 @@ auto get_random_number(const std::size_t min, const std::size_t max)
 void GeneticSearch::getNewSettings() {
     static bool bootstrapping{true};
     if (bootstrapping) {
-        // we are still bootstrapping, so just get a random selection.
-        for (auto& v : vars) { v.second.get_next_neighbor(); }
-        return;
+        if (population.size() >= population_size) {
+            bootstrapping = false;
+        } else {
+            // we are still bootstrapping, so just get a random selection.
+            for (auto& v : vars) { v.second.get_next_neighbor(); }
+            return;
+        }
     }
+    std::cout << "Have population of " << population.size() << " to evaluate!" << std::endl;
     // time to cull the herd?
     if (population.size() >= population_size) {
         std::cout << "Have population of " << population.size() << " to evaluate!" << std::endl;
@@ -102,13 +107,12 @@ void GeneticSearch::getNewSettings() {
         // ...then drop half of them - the "weakest" ones.
         population.erase(population.cbegin() + crossover, population.cend());
         std::cout << "Now have population of " << population.size() << std::endl;
-        bootstrapping = false;
     }
     // We want to generate a new individual using two "high quality" parents.
     // choose parent A
-    individual& A = population[get_random_number(0,crossover)];
+    individual& A = population[get_random_number(0,crossover-1)];
     // choose parent B
-    individual& B = population[get_random_number(0,crossover)];
+    individual& B = population[get_random_number(0,crossover-1)];
     // blend their variables into a new individual and maybe mutate?
     size_t i = 0;
     for (auto& v : vars) {