Neural Morphogenetic Evolution System (NMES)

Blueprint

The Blueprint is the core component encapsulating the entire neural network. It manages neurons, quantum neurons, input and output nodes, and activation functions.

• Neurons: Represent individual processing units within the network.
• QuantumNeurons: Specialized neurons that may leverage quantum computing principles.
• InputNodes & OutputNodes: Define the entry and exit points for data within the network.
• ScalarActivationMap: Maps activation function names to their corresponding functions.

Key Functionalities:

• Initialization: Sets up the foundational structure of the network.
• Loading Neurons: Imports neuron configurations from JSON data.
• Adding Nodes: Facilitates the dynamic addition of input and output nodes.
• Activation Application: Applies specified activation functions to neuron outputs.
• Forward Propagation: Processes inputs through the network over defined timesteps.
• Mutation: Evolves the network structure by inserting new neurons and altering connections.

Model Metadata

ModelMetadata holds comprehensive information about each AI model within NMES, including:

• Basic Information: Model ID, project name, descriptions, parent and child model relationships, creation and modification timestamps.
• Neuronal Structure: Total number of neurons and layers, range of layers and neurons per layer.
• Performance Metrics: Training and testing accuracies, error counts, forgiveness thresholds, etc.
• Session Information: (Planned) Details about training and testing sessions.
• Optimization Settings: Possible mutations, bias and weight adjustment increments.
• Advanced Metadata: Optimization targets, compatible environments, tags, neuron types, attention mechanisms, dropout usage.
• Resource Requirements: Estimated memory usage and compute time.

This metadata is crucial for tracking the evolution and performance of models over time. For more detailed information, refer to the GitHub repository.

Neuron

A Neuron represents a single processing unit within the network, capable of various functionalities based on its type. Key attributes include:

• ID: Unique identifier for the neuron.
• Type: Defines the neuron's role (e.g., Dense, RNN, LSTM, CNN, Dropout, BatchNorm, Attention, NCA).
• Value: Current activation value of the neuron.
• Bias: Offset applied during activation.
• Connections: Links to other neurons, each with associated weights.
• Activation: Specifies the activation function used.
• Additional Fields: Depending on the type, neurons may have specialized attributes such as GateWeights for LSTM, BatchNormParams for Batch Normalization, AttentionWeights for Attention mechanisms, Kernels for CNNs, and state vectors for NCA neurons.

Neuron Processing:

Neurons process inputs based on their type, applying appropriate activation functions and handling specialized behaviors like gating in LSTMs or convolution operations in CNNs. For implementation details, please visit the GitHub repository.

Mutations

Mutations are evolutionary operations that modify the network's structure to explore new configurations and optimize performance. Key functionalities include:

• Insertion of Neurons: Adds new neurons of specified types between input and output nodes, adjusting connections accordingly.
• Initialization of Specialized Fields: Sets up neuron-specific parameters post-insertion, such as GateWeights for LSTM neurons or BatchNormParams for BatchNorm neurons.
• Evolutionary Strategies: Implements various evolutionary algorithms to guide the mutation process, ensuring a balance between exploration and exploitation.

Mutations are central to the neuroevolutionary aspect of NMES, driving the continuous improvement and diversification of the network. For more information on mutations, visit the GitHub repository.

Morphogenetic Decoder

The Morphogenetic Decoder is responsible for transforming DNA-like structures into actionable instructions that generate 2D or 3D components of agents. This process is inspired by biological morphogenesis but employs simplified, abstracted models to facilitate efficient and practical structure generation for engineering applications.

Key Features:

• Genetic Encoding Interpretation: Decodes genetic information to determine the structure and configuration of agents.
• Structure Generation: Produces tangible components (e.g., limbs, geometric shapes) that form the agent's body.
• Integration with Blueprint: Interfaces with the Blueprint to incorporate generated structures into the neural network.

Comparable Systems:

• Karl Sims' Evolved Virtual Creatures: Pioneering work in evolving virtual organisms with both morphology and control systems.
• Compositional Pattern Producing Networks (CPPNs): Networks that generate complex patterns and structures based on genetic encoding.
• L-Systems: Formal grammars used to model the growth processes of plant development and other biological structures.

For implementation specifics, refer to the GitHub repository.

DNA Generation System

The DNA Generation System acts as a generative model, creating new DNA sequences that define the morphology of agents. This system employs various evolutionary algorithms to ensure diversity and optimization.

Potential Implementations:

• Genetic Algorithms (GAs): Utilize selection, crossover, and mutation to evolve DNA sequences over generations.
• Evolutionary Strategies (ES): Focus on optimizing continuous parameters within DNA structures.
• Quality-Diversity Algorithms (e.g., MAP-Elites): Explore and maintain a diverse set of high-quality solutions.
• Novelty Search: Encourages exploration of novel and unique DNA configurations to prevent premature convergence.

Functionality:

• Generation of Viable DNA: Ensures that produced DNA sequences are capable of generating functional and diverse agent structures.
• Diversity Preservation: Maintains a wide variety of DNA configurations to explore a broad search space.
• Integration with Morphogenetic Decoder: Feeds generated DNA into the Morphogenetic Decoder for structure creation.

Detailed implementation can be found in the GitHub repository.

Neural Controller Evolution

The Neural Controller Evolution component designs and evolves the neural networks that govern the behavior and interactions of the generated structures. This ensures that agents not only have diverse morphologies but also possess optimized control systems to achieve their goals.

Key Implementations:

• Neural Architecture Search (NAS): Automates the design and optimization of neural network architectures.
• HyperNEAT-like Indirect Encoding: Encodes neural networks in a way that captures structural patterns and symmetries.
• Adaptive Topology Evolution: Dynamically adjusts the network topology to better fit the agent's morphology and task requirements.
• Weight Optimization: Fine-tunes the weights of neural connections to enhance performance.

Functionality:

• Co-Evolution of Structure and Control: Simultaneously evolves both the physical structure and the neural controllers to achieve synergistic improvements.
• Performance Evaluation: Assesses the effectiveness of neural controllers based on predefined fitness criteria.
• Integration with Meta-Optimization Layer: Collaborates with the meta-optimization layer to guide evolutionary strategies and parameter adjustments.

For more details on neural controller evolution, please visit the GitHub repository.

Meta-Optimization Layer

The Meta-Optimization Layer oversees the incremental improvement processes within NMES, ensuring that evolutionary strategies are effectively driving progress toward defined goals.

Potential Implementations:

• Population-Based Training: Manages multiple populations of agents, facilitating competition and cooperation.
• Multi-Objective Optimization: Balances multiple fitness criteria, such as speed, efficiency, and adaptability.
• Surrogate Model-Based Optimization: Uses surrogate models to predict fitness outcomes, reducing computational overhead.
• Gradient-Free Optimization Methods: Employs optimization techniques that do not rely on gradient information, suitable for complex and non-differentiable fitness landscapes.

Functionality:

• Management of Evolutionary Processes: Coordinates the selection, mutation, and crossover operations across different components.
• Incremental Learning: Facilitates the gradual improvement of agents by integrating learning mechanisms over generations.
• Performance Tracking: Monitors and records the progress of evolutionary processes, providing insights for further optimization.

Explore the Meta-Optimization Layer in detail by visiting the GitHub repository.

Initializing the Blueprint

The Blueprint serves as the foundation of the neural network within NMES. To initialize a new Blueprint, follow the instructions in the GitHub repository.

Loading Neurons

Neurons are essential building blocks of the neural network. To load neurons from a JSON string, refer to the code examples provided in the GitHub repository.

Adding Input and Output Nodes

Defining input and output nodes is crucial for establishing data flow within the network. Detailed instructions and code snippets are available in the GitHub repository.

Running the Network

To execute the neural network and propagate inputs through it, follow the guidelines and examples provided in the GitHub repository.

Mutating the Network

Evolving the network structure is facilitated through mutations. To insert new neurons and evolve the network, refer to the mutation strategies outlined in the GitHub repository.

Example 1: Basic Network Initialization and Execution

This example demonstrates the basic steps to initialize a Blueprint, load neurons, add input and output nodes, run the network, and mutate the network. For the complete implementation, please visit the GitHub repository.

Example 2: Evolving a CNN Neuron

This example illustrates how to evolve a Convolutional Neural Network (CNN) neuron within the network. Detailed steps and code snippets are available in the GitHub repository.

Evolved Virtual Creatures

• Karl Sims' Evolved Virtual Creatures: Early work in evolving both morphology and control systems for virtual organisms, demonstrating the potential for complex behavior emergence.

Artificial Life Systems

• Polyworld: Simulates a virtual ecosystem where digital organisms evolve and interact.
• Avida: A digital evolution platform where self-replicating programs evolve to perform computational tasks.

Developmental Systems

• AEvol: Focuses on the co-evolution of body plans and neural controllers in digital organisms.
• Framsticks: A framework for evolving virtual creatures with flexible morphologies and control systems.

Evolutionary Robotics

• Sodarace: An evolutionary competition where participants design 2D creatures that navigate obstacles, evolving both structure and control.

Neural Architecture Search (NAS)

• AutoML by Google: Utilizes NAS to automatically design efficient neural network architectures for various tasks.
• NASNet: Implements reinforcement learning-based NAS to discover high-performing convolutional neural networks.

Procedural Content Generation (PCG)

• Wave Function Collapse: An algorithm for generating patterns and structures based on input constraints.
• GANs for PCG: Employ Generative Adversarial Networks to create realistic and diverse game assets.

Search Space Complexity

Description: The combined search space for both morphology and neural architectures is vast and highly complex.

Solutions:

• Implement efficient exploration strategies: Such as Novelty Search to explore diverse configurations.
• Utilize dimensionality reduction techniques: To manage and navigate the search space effectively.

Fitness Landscape

Description: The fitness landscape is highly non-linear with numerous local optima, making optimization difficult.

Solutions:

• Employ diversity preservation mechanisms: To maintain a varied population of solutions.
• Use multi-objective optimization: To balance different fitness criteria and avoid premature convergence.

Implementation Challenges

• Physics Simulations: Accurately simulating physical interactions for evolved morphologies requires robust and efficient simulation environments.
• Computational Intensity: Neuroevolution and NAS are computationally demanding, necessitating optimized algorithms and possibly distributed computing resources.
• Scalability: As the system evolves, maintaining performance and managing increasing complexity is essential.

Integration Complexity

Description: Seamlessly integrating multiple AI methodologies (neuroevolution, NAS, procedural generation) is inherently complex.

Solutions:

• Maintain a modular architecture: To isolate and manage different components.
• Develop clear interfaces and communication protocols: Between modules to ensure smooth interactions.

For more insights into handling integration complexities, check the GitHub repository.

Glossary

• Neuroevolution: The use of evolutionary algorithms to develop and optimize neural networks.
• Neural Architecture Search (NAS): Automated process of designing neural network architectures.
• Morphogenesis: Biological process that causes an organism to develop its shape.
• LSTM: Long Short-Term Memory, a type of recurrent neural network.
• CNN: Convolutional Neural Network, often used for image processing.
• Batch Normalization: Technique to normalize the inputs of each layer to improve training speed and stability.
• Dropout: Regularization technique to prevent overfitting by randomly dropping neurons during training.
• NCA: Neural Cellular Automata, models that simulate cellular processes using neural networks.

References

• Stanley, K. O., & Miikkulainen, R. (2002). Evolving Neural Networks through Augmenting Topologies. Evolutionary Computation.
• Hinton, G. E., Srivastava, N., & Swersky, K. (2012). Neural Networks for Machine Learning. Lecture Notes.
• Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative Adversarial Nets. Advances in Neural Information Processing Systems.
• Lehman, J., & Stanley, K. O. (2011). Abandoning Objective Traps: Moving Beyond Single Objective Optimization in Evolutionary Computation. Proceedings of the Genetic and Evolutionary Computation Conference.

Note: Update references with relevant literature as your project progresses.