readd generational replacement, add constant

src/common.odin

@@ -14,6 +14,7 @@ CROSSOVER_RATE :: 0.7
 MUTATION_RATE :: 0.015
 RANDOM_SEED :: u64(42)
 OUTPUT_FILE :: "output/data.csv"
+SURVIVOR_SELECTION_POLICY :: probabilistic_crowding
 
 Problem :: struct {
 	name:            string,

src/ga.odin

@@ -407,3 +407,53 @@ write_results :: proc(filename: string, stats: []Stats) -> bool {
 	csv.writer_flush(&w)
 	return true
 }
+
+// Standard generational replacement (no crowding)
+generational_replacement :: proc(
+	pop: ^Population,
+	offspring: ^Population,
+	pop_fitnesses: []f64,
+	offspring_fitnesses: []f64,
+	maximize: bool,
+) -> Population {
+	next_gen: Population
+
+	// Apply elitism
+	elite_idx := get_best_indices(pop_fitnesses[:], ELITISM_COUNT, maximize)
+	defer delete(elite_idx)
+
+	for i in 0 ..< ELITISM_COUNT {
+		next_gen[i] = clone_chromosome(pop[elite_idx[i]])
+	}
+
+	// Rest are offspring (standard replacement)
+	for i := ELITISM_COUNT; i < POPULATION_SIZE; i += 1 {
+		next_gen[i] = clone_chromosome(offspring[i])
+	}
+
+	return next_gen
+}
+
+get_best_indices :: proc(fitnesses: []f64, count: int, maximize: bool) -> []int {
+    indices := make([]int, len(fitnesses))
+    for i in 0 ..< len(indices) {
+        indices[i] = i
+    }
+
+    // Partial sort for top N
+    for i in 0 ..< count {
+        best := i
+        for j in i + 1 ..< len(indices) {
+            is_better := maximize ? fitnesses[indices[j]] > fitnesses[indices[best]] : fitnesses[indices[j]] < fitnesses[indices[best]]
+            if is_better {
+                best = j
+            }
+        }
+        indices[i], indices[best] = indices[best], indices[i]
+    }
+
+    result := make([]int, count)
+    copy(result, indices[:count])
+    delete(indices)
+    return result
+}

src/main.odin

@@ -33,5 +33,5 @@ main :: proc() {
 		fmt.printfln("RMSE: %.4f\n", baseline)
 	}
 
-	run_ga(problem, deterministic_crowding)
+	run_ga(problem, SURVIVOR_SELECTION_POLICY)
 }