Add crossover spawning of new boppies

Controllers/GameController/GameController.gd

@@ -117,11 +117,11 @@ func handle_boppie(boppie):
 func _on_SpawnNewBoppie(at, dna):
 	add_boppie(at, boppie_configurations[0].scene, dna)
 
-func add_boppie(at: Vector2, scene: PackedScene, dna=null):
+func add_boppie(at: Vector2, scene: PackedScene, dna=null, dna2=null):
 	var instance = scene.instance()
 	if instance is Boppie:
 		if dna != null:
-			instance.set_dna(dna, 1)
+			instance.set_dna(dna, 1, dna2)
 		handle_boppie(instance)
 	instance.rotation = Globals.rng.randf() * 2 * PI
 	add_child(instance)
@@ -134,10 +134,15 @@ func add_boppie(at: Vector2, scene: PackedScene, dna=null):
 
 func add_random_boppies(count: int, config: BoppieConfiguration):
 	for _i in range(count):
-		var old_dna = null
-		if config.fittest.size() == keep_n_fittest_boppies and Globals.rng.randf() > config.new_dna_chance:
-			old_dna = config.fittest[Globals.rng.randi() % keep_n_fittest_boppies][1]
-		add_boppie(random_world_coordinate(), config.scene, old_dna)
+		var dna1 = null
+		var dna2 = null
+		if config.fittest.size() >= 10 and Globals.rng.randf() > config.new_dna_chance:
+			var fittest_len = len(config.fittest)
+			var dna1_index = Globals.rng.randi() % fittest_len
+			var dna2_index = (dna1_index + (fittest_len - 1)) % fittest_len
+			dna1 = config.fittest[dna1_index][1]
+			dna2 = config.fittest[dna2_index][1]
+		add_boppie(random_world_coordinate(), config.scene, dna1, dna2)
 
 
 func add_food(at: Vector2):

Entities/Boppie/Boppie.gd

@@ -180,7 +180,6 @@ func initialize_dna():
 func set_dna(new_dna: Dictionary, mutation_factor=0, crossover_dna=null):
 	new_dna = new_dna.duplicate(true)
 	for property in new_dna:
-		print(property)
 		var resolved_subproperty = self
 		var subproperties = property.split(".")
 		var last = subproperties[-1]
@@ -192,7 +191,6 @@ func set_dna(new_dna: Dictionary, mutation_factor=0, crossover_dna=null):
 			resolved_subproperty.crossover(property, crossover_dna[property])
 		if mutation_factor > 0:
 			resolved_subproperty.mutate(last, new_dna["offspring_mutability"] * mutation_factor)
-	print()
 	initialize_dna()
 
 func mutate(property: String, mutability: float):

README.md

@@ -63,14 +63,10 @@ Quickstart: after starting a simulation: press `9` to simulate quickly. After a
 * Open Godot and import the `project.godot` file
 * Now press on the play button to run it 
 
-## Ideas for features
+## Ideas for future features
 
 ### Simulation
 
-* Implement NEAT (Neuroevolution of Augmenting Topologies), such that the neural networks of the boppies change with each generation
-    * Innovation number (tracking of changes between species)
-    * Different parallel simulations where the best are merged into 1
-    * Merging of neural networks
 * Add obstacles
 * Add more senses for the boppies, such as:
     * Instead of rays try neural network inputs with information for angle+distance
@@ -80,36 +76,53 @@ Quickstart: after starting a simulation: press `9` to simulate quickly. After a
 * Add sexual reproduction, as in the real world merging DNA from two individuals has greatly benefited survival of the fittest
 * Add loading/saving of simulations
 * Add areas of high ground productivity (where more food spawns)
+* Add (for example), a river in the middle of the map, which separates the species on the left/right of it. 
+* Encode how many creatures are reproduced in the DNA of the creature
 * Make boppies leave flesh after death
 * Change meat-eating from boolean to a float, where it essentially becomes meat-tolerance or meat effectiveness (a factor of how much energy can be gained from meat). However high meat-tolerance means low 
-* Add/extend boppie color as part of DNA 
+* Extend boppie color as part of DNA 
     * Ideas: 
         * HSL (where H is part of DNA, S shows energy, L = .5 or meat-tolerance )
         * RGB (where R, G is part of DNA, B shows meat-tolerance)
 * Add reinforcement learning
     * Save which neurons most contributed to a reward in the last seconds (eating)/penalty (taking damage), increase the weights of those connections
-* Drop in boppies from fitness list
-* Show genetic tree for entire simulation
+* Different parallel simulations where the best are merged into one
 
 ### UI/UX
 * Add menu so that simulation could be configured
 * Show additional senses (dangersense) near boppie
+* Show genetic tree for entire simulation
+* Improve graph by drawing lines instead of pixels
+* Add more graphs 
+    * Fittest creature each second
+    * Species stacked bar-plot
+* Select different profiles for displaying neural network (weights or activations)
+* Full screen on neural network, which shows actual weights
+* Improve display of recurrent connections
+* Check DNA importing, such that no object references are mentioned in the DNA
+* Use textures on the ground (fertile vs infertile land)
+* Disable spike rotation and blood in performance mode
+
 
 ## Changelog
 
-### v0.2.0 (not yet released)
+### v0.2.0 (2022-02-17)
 
+* UI/UX Improvements
+    * Random seed is shown 
+    * Can now drag boppies from fittest list into world
+    * Camera can now be moved by dragging the mouse
+    * DNA/Neural network as tabs, as they did not fit in a single column
+    * Neural networks now show what each input/output neuron means
 * Neural networks can have any structure (not necessarily fully connected)
-* Added an innovation number for each change in the neural network, as described in NEAT
 * Added rotating traps with spikes and blood marks after death
 * Added these senses for boppies:
     * Danger sense (for detecting traps and kloppies)
-* Camera can now be moved by dragging the mouse
-* Random seed is shown 
-* Neural networks now show what each input/output neuron means
-* DNA/Neural network as tabs, as they did not fit in a single column
 * Added boppie color as part of DNA 
-* Can now drag boppies from fittest list into world
+* Implemented NEAT (Neuroevolution of Augmenting Topologies), such that the neural networks of the boppies change with each generation
+    * Added an innovation number for each connection in the neural network, as described in NEAT
+    * Crossover of creature DNA and neural networks
+
 
 ### v0.1.0 (2021-10-19)
 

UI/GameUI/Neurons.gd

@@ -77,9 +77,8 @@ func _draw():
 			var left_pos = lookup_index_to_pos[connection[1][i]]
 			var weight = connection[1][i+1]
 			var dendron = weight * values[connection[1][i]]
-			var color = neuron_weight_gradient.interpolate((weight + 1) / 2.0)
-			# var color = Color.white
-			draw_line(left_pos, right_pos, color, abs(weight * 3))
+			var color = neuron_weight_gradient.interpolate((dendron + 1) / 2.0)
+			draw_line(left_pos, right_pos, color, abs(dendron * 3))
 
 	# Draw neurons
 	for index in lookup_index_to_pos: