Refactor: Simplify EvoNet initializers (topology-only, breaking)

README.md

@@ -105,7 +105,6 @@ modules:
     type: evonet
     dim: [4, 6, 2]
     activation: [linear, tanh, tanh]
-    initializer: normal_evonet
     mutation:
       strategy: constant
       probability: 1.0

docs/config_guide.md

@@ -145,7 +145,6 @@ modules:
     type: evonet
     dim: [2, 0, 0, 1]       # hidden layers start empty
     activation: [linear, tanh, tanh, sigmoid]
-    initializer: normal_evonet
     weights:
       initializer: normal
       std: 0.5
@@ -161,7 +160,7 @@ modules:
     # If `recurrent` is not enabled, this block has no effect.
     delay:
       initializer: uniform   # uniform | fixed
-      bounds: [1, 8]        # only for random
+      bounds: [1, 8]
       # value: 3            # only for fixed
 
     mutation:

docs/config_parameter.md

@@ -288,7 +288,7 @@ modules:
 |-------------------|---------------------|---------|-------------|
 | `dim`             | list[int]           | —       | Layer sizes, e.g. `[4, 0, 0, 2]`. Hidden layers can start empty (0) and grow through structural mutation. |
 | `activation`      | str \| list[str]    | —       | If list: activation per layer. If str: used for non-input layers; input layer is treated as linear. |
-| `initializer`     | str                 | —       | Network initialization method (e.g. `normal_evonet`, `unconnected_evonet`). |
+| `initializer`     | str                 | default_evonet       | Topology preset (e.g. `default_evonet`, `unconnected_evonet`, `identity_evonet`). Parameter initialization is configured via `weights`, `bias`, and `delay`. |
 | `weights`         | dict                | —       | Weight init and bounds configuration (initializer, bounds, optional params). | 
 | `bias`            | dict                | —       | Bias init and bounds configuration (initializer, bounds, optional params). | 
 | `neuron_dynamics` | list[dict] \| null  | null    | Optional per-layer neuron dynamics specification. Must match `len(dim)`. |
@@ -299,30 +299,34 @@ modules:
 |Parameter	 | Type	 | Default	 | Explanation |
 |------------|-------|-----------|-------------|
 |initializer | str   | "normal"	 | Weight initializer preset (normal, uniform, zero, …). |
-|bounds	     | list[float]	| [-1.0, 1.0]	| Hard clipping bounds [min_w, max_w]. |
+|bounds	     | list[float]	| [-0.5, 0.5]	| Hard clipping bounds. |
 |std         | float | null	|null | Std-dev for normal (if used). |
 
 ##### bias block
 |Parameter	 | Type	 | Default	 | Explanation |
 |------------|-------|-----------|-------------|
-|initializer | str   | "normal"	 | Weight initializer preset (normal, uniform, zero, …). |
-|bounds	     | list[float]	| [-1.0, 1.0]	| Hard clipping bounds [min_w, max_w]. |
-|std         | float | null	|null | Std-dev for normal (if used). |
+|initializer | str   | "normal"	 | Bias initializer preset (normal, uniform, zero, …). |
+|bounds	     | list[float]	| [-1.0, 1.0]	| Hard clipping bounds. |
+|std         | float | null	| Std-dev for normal (if used). |
 
 ---
 
-#### EvoNet Initializer
+#### EvoNet Initializer (Topology Presets)
+
+`initializer` selects a **topology preset**.  
 
-The EvoNet module uses:
+Parameter initialization is configured via:
+- `weights`
+- `bias`
+- `delay` (recurrent connections only)
 
-| Initializer         | Weights                            | Biases                           | Notes                                       |
-|---------------------|------------------------------------|----------------------------------|---------------------------------------------|
-| `normal_evonet`     | Normal(0, 0.5)                     | Normal(0, 0.5)                   | Default initializer for general use         |
-| `unconnected_evonet`| None                               | 0                                | For pure structural growth; empty topology  |
-| `random_evonet`     | Random                             | Uniform(bias bounds)             | For broader stochastic exploration           |
-| `zero_evonet`       | 0                                  | 0                                | Deterministic baseline; debugging            |
-| `identity_evonet`   | Small random                       | Small random                     | Designed for stable recurrent memory         |
+Allowed presets:
 
+| Initializer            | Meaning (topology only) |
+|------------------------|-------------------------|
+| `default_evonet`       | Standard EvoNet topology preset (uses `connection_scope`, `connection_density`, and `recurrent`). |
+| `unconnected_evonet`   | Creates neurons/layers but starts with **no connections** (use structural mutation to grow). |
+| `identity_evonet`      | Special preset intended for stable recurrent memory (may override parameters internally; see notes below). |
 
 ---
 
@@ -348,7 +352,6 @@ modules:
     type: evonet
     dim: [1, 16, 1]
     activation: [linear, tanh, sigmoid]
-    initializer: normal_evonet
     neuron_dynamics:
       - name: standard
         params: {}
@@ -378,7 +381,7 @@ modules:
     ...
     delay:
       initializer: uniform  # uniform | fixed
-      bounds: [1, 8]        # only for random
+      bounds: [1, 8]
       # value: 3            # only for fixed
 ```
 

evolib/config/evonet_component_config.py

@@ -189,7 +189,6 @@ class EvoNetComponentConfig(BaseModel):
             type: evonet
             dim: [4, 6, 2]                       # input, hidden, output
             activation: [linear, relu, sigmoid]  # single activation or list per layer
-            initializer: normal_evonet           # weight/bias initializer
 
             weights:
               initializer: normal
@@ -228,7 +227,9 @@ class EvoNetComponentConfig(BaseModel):
     recurrent: Optional[Literal["none", "direct", "local", "all"]] = "none"
 
     # Name of the initializer function (resolved via initializer registry)
-    initializer: str = Field(..., description="Name of the initializer to use")
+    initializer: str = Field(
+        default="default_evonet", description="Name of the initializer to use"
+    )
 
     # Connection topology for initialization
     connection_scope: Literal["adjacent", "crosslayer"] = Field(
@@ -266,6 +267,29 @@ class EvoNetComponentConfig(BaseModel):
     structural: Optional[StructuralMutationConfig] = None
 
     # Validators
+    @field_validator("initializer")
+    @classmethod
+    def validate_initializer(cls, name: str) -> str:
+        """
+        Validate that the initializer is one of the allowed topology presets.
+
+        Parameter initialization is handled exclusively via weights/bias/delay blocks.
+        """
+        allowed = {
+            "default_evonet",
+            "unconnected_evonet",
+            "identity_evonet",
+        }
+
+        if name not in allowed:
+            raise ValueError(
+                f"Unknown EvoNet initializer '{name}'. "
+                f"Allowed values: {sorted(allowed)}. "
+                "Parameter initialization is configured via "
+                "'weights', 'bias', and 'delay'."
+            )
+
+        return name
 
     @field_validator("neuron_dynamics")
     @classmethod

evolib/initializers/evonet_initializers.py

@@ -173,28 +173,7 @@ def _build_architecture(
         )
 
 
-def initializer_unconnected_evonet(config: FullConfig, module: str) -> EvoNet:
-    """
-    Initializes an EvoNet without connections.
-
-    Args:
-        config (FullConfig): Full experiment configuration
-        module (str): Module name (e.g. "brain")
-
-    Returns:
-        EvoNet: Initialized EvoNet representation
-    """
-    para = EvoNet()
-    cfg = config.modules[module].model_copy(deep=True)
-    para.apply_config(cfg)
-
-    _build_architecture(para, cfg, connection_init="none")
-    _apply_bias_init(para, cfg)
-
-    return para
-
-
-def initializer_normal_evonet(config: FullConfig, module: str) -> EvoNet:
+def initializer_default_evonet(config: FullConfig, module: str) -> EvoNet:
     """
     Build a standard EvoNet architecture and initialize parameters according to the
     explicit configuration blocks.
@@ -203,9 +182,6 @@ def initializer_normal_evonet(config: FullConfig, module: str) -> EvoNet:
     - Weights are initialized using `cfg.weights`.
     - Biases are initialized using `cfg.bias`.
     - Delay (if configured) is initialized using `cfg.delay`.
-
-    No implicit parameter initialization is performed here.
-    All parameter distributions are controlled explicitly via the config.
     """
 
     para = EvoNet()
@@ -219,36 +195,23 @@ def initializer_normal_evonet(config: FullConfig, module: str) -> EvoNet:
     return para
 
 
-def initializer_random_evonet(config: FullConfig, module: str) -> EvoNet:
-    """
-    Backward-compatible alias for the standard EvoNet initializer. Will be removed.
-
-    Parameter initialization is controlled by `cfg.weights`, `cfg.bias`,
-    and `cfg.delay`.
-    """
-    return initializer_normal_evonet(config, module)
-
-
-def initializer_zero_evonet(config: FullConfig, module: str) -> EvoNet:
+def initializer_unconnected_evonet(config: FullConfig, module: str) -> EvoNet:
     """
-    Build a standard EvoNet architecture and initialize all parameters to zero.
+    Initializes an EvoNet without connections.
 
-    - All connection weights are set to 0.
-    - All biases are set to 0.
-    - Delay initialization follows `cfg.delay` if applicable.
+    Args:
+        config (FullConfig): Full experiment configuration
+        module (str): Module name (e.g. "brain")
 
-    This initializer ignores `cfg.weights` and `cfg.bias` distributions.
+    Returns:
+        EvoNet: Initialized EvoNet representation
     """
-
     para = EvoNet()
     cfg = config.modules[module].model_copy(deep=True)
     para.apply_config(cfg)
 
-    _build_architecture(para, cfg, connection_init="zero")
-    _apply_delay_init(para, cfg)
-
-    para.net.set_weights(np.zeros(para.net.num_weights))
-    para.net.set_biases(np.zeros(para.net.num_biases))
+    _build_architecture(para, cfg, connection_init="none")
+    _apply_bias_init(para, cfg)
 
     return para
 

evolib/initializers/registry.py

@@ -17,11 +17,9 @@
 
 # EvoNet-based initializer
 from evolib.initializers.evonet_initializers import (
+    initializer_default_evonet,
     initializer_identity_evonet,
-    initializer_normal_evonet,
-    initializer_random_evonet,
     initializer_unconnected_evonet,
-    initializer_zero_evonet,
 )
 
 # NetVector-based initializer
@@ -50,9 +48,7 @@
     "fixed_vector": initializer_fixed_vector,
     "adaptive_vector": initializer_adaptive_vector,
     "normal_net": initializer_normal_net,
-    "normal_evonet": initializer_normal_evonet,
-    "random_evonet": initializer_random_evonet,
-    "zero_evonet": initializer_zero_evonet,
+    "default_evonet": initializer_default_evonet,
     "identity_evonet": initializer_identity_evonet,
     "unconnected_evonet": initializer_unconnected_evonet,
 }

examples/07_evonet/configs/01_sine_approximation.yaml

@@ -14,7 +14,6 @@ modules:
     type: evonet
     dim: [1, 6, 6, 1]
     activation: ["tanh", "tanh", "tanh", "tanh"]
-    initializer: normal_evonet
 
     weights:
       initializer: normal

examples/07_evonet/configs/02_sine_delay.yaml

@@ -16,7 +16,6 @@ modules:
     type: evonet
     dim: [1, 1, 1]
     activation: ["linear", "linear", "linear"]
-    initializer: normal_evonet
 
     weights:
       initializer: normal

examples/07_evonet/configs/03_delay_bitseq_echo.yaml

@@ -16,7 +16,6 @@ modules:
     type: evonet
     dim: [1, 3, 1]
     activation: ["linear", "tanh", "sigmoid"]
-    initializer: normal_evonet
 
     weights:
       initializer: normal

examples/07_evonet/configs/04_temporal_smoothing_leaky.yaml

@@ -14,7 +14,6 @@ modules:
     type: evonet
     dim: [1, 6, 1]
     activation: [linear, tanh, sigmoid]
-    initializer: random_evonet
 
     weights:
       initializer: uniform

examples/07_evonet/configs/04_temporal_smoothing_standard.yaml

@@ -14,7 +14,6 @@ modules:
     type: evonet
     dim: [1, 6, 1]
     activation: [linear, tanh, sigmoid]
-    initializer: random_evonet
 
     weights:
       initializer: uniform

examples/07_evonet/configs/05_image_approximation.yaml

@@ -17,7 +17,6 @@ modules:
     type: evonet
     dim: [2, 16, 8, 1]
     activation: ["linear", "tanh", "tanh", "sigmoid"]
-    initializer: normal_evonet
 
     weights:
       initializer: normal

examples/07_evonet/configs/07_recurrent_bit_prediction.yaml

@@ -17,7 +17,6 @@ modules:
     dim: [1, 8, 1]
     activation: [linear, tanh, sigmoid]
     recurrent: "local"
-    initializer: zero_evonet
 
     weights:
       initializer: zero

examples/08_gym/configs/05_bipedal_walker.yaml

@@ -3,6 +3,10 @@ offspring_pool_size: 250
 max_generations: 200
 num_elites: 5
 
+parallel:
+  backend: ray
+  address: ray://10.17.5.10:10001
+
 evolution:
   strategy: mu_plus_lambda
 
@@ -13,16 +17,15 @@ modules:
     dim: [24, 8, 4]         # 24 inputs (state), 4 outputs (joint torques)
     activation: [linear, tanh, linear]
     recurrent: direct
-    initializer: normal_evonet
 
     weights:
       initializer: normal
-      std: 0.2
+      std: 0.5
       bounds: [-1.0, 1.0]
 
     bias:
       initializer: normal
-      std: 0.1
+      std: 0.5
       bounds: [-0.5, 0.5]
 
     mutation:

tests/operators/test_structural_mutation.py

@@ -7,7 +7,7 @@
     StructuralTopology,
 )
 from evolib.config.schema import FullConfig
-from evolib.initializers.evonet_initializers import initializer_normal_evonet
+from evolib.initializers.evonet_initializers import initializer_default_evonet
 from evolib.operators.evonet_structural_mutation import (
     mutate_structure,
 )
@@ -28,7 +28,6 @@ def make_minimal_evonet() -> EvoNet:
                 "type": "evonet",
                 "dim": [2, 3, 1],
                 "activation": ["linear", "tanh", "sigmoid"],
-                "initializer": "normal_evonet",
                 "weights": {
                     "initializer": "normal",
                     "std": 0.5,
@@ -48,7 +47,7 @@ def make_minimal_evonet() -> EvoNet:
         },
     }
     full_config = FullConfig.model_validate(config_dict)
-    return initializer_normal_evonet(full_config, module="brain")
+    return initializer_default_evonet(full_config, module="brain")
 
 
 def test_add_connection() -> None: