Neuroevolution

[csmangum]

Experimenting with Neuroevolution

Description

This issue aims to explore the implementation and experimentation of neuroevolution techniques within the project. Neuroevolution represents a promising avenue for optimizing neural networks through evolutionary algorithms, diverging from traditional gradient descent methods.

Objectives

• Evaluate the implementation of basic neuroevolution strategies using PyTorch.
• Explore advanced encoding techniques for efficient evolution of complex network architectures.
• Investigate the impact of maintaining diverse populations through mechanisms like fitness sharing and novelty search.
• Experiment with evolving not just the architectures but also learning rules or hyperparameters.
• Assess the feasibility of hybrid models that combine evolutionary strategies with gradient-based optimization.

Proposed Methodology

1. Define Neural Network Structure: Establish a flexible neural network model in PyTorch to serve as the base for evolution.
2. Setup Evolutionary Algorithm: Implement an evolutionary algorithm framework that includes population initialization, fitness evaluation, selection, crossover, mutation, and generation replacement.
3. Evolution Process Experimentation:
   • Selection: Experiment with different selection strategies to identify top-performing networks.
   • Crossover and Mutation: Implement and test various approaches for network crossover and mutation to generate offspring.
   • Diversity Maintenance: Incorporate techniques to ensure or increase population diversity across generations.
4. Hybrid Approach Exploration: Explore potential hybrid approaches, where evolution optimizes network architecture and hyperparameters, while gradient descent is used for network training.
5. Parallelization and Efficiency: Leverage PyTorch’s parallel computation capabilities to enhance the efficiency of the evolutionary process.

Considerations

• Determine appropriate fitness functions for evaluating network performance based on our project's goals.
• Consider the computational resources required for extensive experiments and potential parallelization strategies.
• Evaluate the scalability of the neuroevolution approach, especially when dealing with complex and large network architectures.

Expected Outcomes

• A benchmark of the performance of neuroevolutionary techniques compared to traditional optimization methods in our context.
• Insights into the advantages and limitations of neuroevolution for our specific project needs.
• Identification of potential hybrid strategies that could yield better performance or efficiency.
• Recommendations for further exploration or integration of neuroevolutionary approaches into our project.

Next Steps

• Literature review on recent neuroevolution techniques and their applications.
• Design initial experiments, including network structures and evolutionary algorithm parameters.
• Implement the evolutionary framework in PyTorch.
• Conduct experiments and document findings.
• Review results and decide on further exploration or integration strategies.