EvoNet

EvoNet is a modular and evolvable neural network core designed for integration with EvoLib.
It supports dynamic topologies, recurrent connections, per-neuron activation, and structural evolution, with explicit and deterministic behaviour.

Scope

EvoNet is not a state-of-the-art or general-purpose deep learning framework.

It does not aim to compete with libraries such as PyTorch, TensorFlow, or JAX in terms of performance, scalability, or training algorithms. Backpropagation, GPU acceleration, and highly optimised tensor operations are outside the scope of this project.

Instead, EvoNet supports evolutionary algorithms, structural mutation, and exploratory research, with a focus on transparent and explicit implementations rather than performance optimisation or feature completeness.

Overview

• Explicit neural network topology with inspectable structure
• Support for recurrent connections and temporal dynamics
• Dynamic topology growth during evolution
• Per-neuron activations and stateful neuron dynamics
• Supports evolutionary and structural mutation methods
• Deterministic execution model with explicit state

Technical Highlights

• Explicit layer / neuron / connection objects
• Typed neuron roles (INPUT, HIDDEN, OUTPUT)
• Typed connection kinds (STANDARD, RECURRENT, …)
• Recurrent connections with integer delay buffers
• Per-neuron activation functions and local dynamics
• Deterministic forward execution (calc() = one time step)
• Runtime topology growth (add/remove neurons and connections)
• Structural mutation utilities (weights, biases, activations, topology)
• Deterministic weight & bias vectorization (get_weights, set_weights)
• YAML / JSON serialization of full topology and parameters
• Graphviz-based network visualization
• Lightweight Python implementation with small runtime dependency

Quick Example

from evonet import Nnet, NeuronRole

net = Nnet()
net.add_layer()  # Input layer
net.add_layer()  # Output layer

net.add_neuron(layer_idx=0, role=NeuronRole.INPUT, activation="linear", connection_init="none", label="in")
net.add_neuron(layer_idx=1, role=NeuronRole.OUTPUT, activation="linear", bias=0.5, label="out")

result = net.calc([1.0])

Recurrent Connections & Delay

from evonet import ConnectionType

net.add_connection(
    src,
    dst,
    weight=0.8,
    conn_type=ConnectionType.RECURRENT,
    delay=2,   # uses output from t-2
)

Semantics:

• delay = 1 → previous time step
• delay > 1 → buffered history
• delay = 0 is normalized to 1 for recurrent edges

A full reset (net.reset(full=True)) clears all delay buffers.

Neuron Dynamics

Neuron behaviour is defined locally at the neuron level:

net.add_neuron(
    layer_idx=1,
    activation="tanh",
    dynamics_name="leaky",
    dynamics_params={"alpha": 0.2},
)

Current built-in dynamics:

• standard – stateless activation
• leaky – leaky integrator using last_output

Dynamics are evaluated inside the neuron, not at network level.

Structural Mutation Utilities

The evonet.mutation module provides mutation operators for:

• mutate weights / biases (Gaussian noise)
• mutate activations (global or layer-specific)
• add/remove random connections
• add/remove random hidden neurons
• control recurrent edge kinds (direct, lateral, indirect)
• neutral or near-zero initialization

EvoNet itself does not decide when or why to mutate — that logic belongs in EvoLib.

Serialization

Network topology, parameters, and configuration can be saved and restored:

net.save("network.yaml")
net = Nnet.load("network.yaml")

Supported formats:

• YAML (recommended, human-readable)
• JSON (machine-friendly)

Serialization includes: layers, neurons, activations, biases, dynamics, connections, types, and delays.

Relationship to EvoLib

• EvoNet: execution, structure, state
• EvoLib: evolution strategy, fitness, mutation scheduling, environments

EvoNet is intentionally usable as a standalone library. Evolutionary control, fitness evaluation, and mutation scheduling are provided by EvoLib.

License

MIT License - see MIT License.