GENESIS: Generative Evolutionary Neural Strategy

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Expert Rating 4.9
Almalgo_Labs
Project Owner

GENESIS: Generative Evolutionary Neural Strategy

Expert Rating

4.9

Overview

GENESIS proposes to systematically investigate the potential of evolutionary computation techniques, for training large-scale transformer models. This research will explore fundamental questions about how evolutionary methods can enhance deep learning: Can they provide more efficient training alternatives to backpropagation? How might they optimize neural architectures more effectively? Through rigorous empirical studies and theoretical analysis, we aim to develop a comprehensive understanding of evolutionary deep learning's capabilities and limitations. Our findings will contribute to the growing field of evolutionary deep learning while advancing Hyperon's neural atomspace framework.

RFP Guidelines

Evolutionary algorithms for training transformers and other DNNs

Complete & Awarded
  • Type SingularityNET RFP
  • Total RFP Funding $40,000 USD
  • Proposals 8
  • Awarded Projects 1
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SingularityNET
Aug. 12, 2024

Explore and demonstrate the use of evolutionary methods (EMs) for training various DNNs including transformer networks. Such exploration could include using EMs to determine model node weights, and/or using EMs to evolve DNN/LLM architectures. Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is an example of one very promising evolutionary method among others.

Proposal Description

Company Name (if applicable)

Almalgo Labs

Project details

Phase 1: Foundational Research and Baseline Establishment

This crucial initial phase establishes our reference points and experimental framework. We'll begin with transformer models of varying sizes, starting with smaller architectures (e.g., ViT-Small) and scaling up to larger models. For baseline establishment, we'll:

  1. Training Implementation:
  • Set up PyTorch/TensorFlow training pipelines for transformer architectures
  • Implement standard backpropagation with Adam optimizer
  • Configure gradient clipping and learning rate scheduling
  • Establish regularization techniques (dropout, weight decay)
  1. Performance Documentation:
  • Track GPU/CPU utilization across training epochs
  • Measure memory consumption patterns
  • Monitor gradient flow and parameter updates
  • Record training/validation curves with detailed metrics
  1. Evaluation Framework:
  • Implement standardized testing across multiple datasets (e.g., ImageNet, CIFAR-100)
  • Create automated benchmarking tools
  • Design comprehensive logging systems
  • Develop visualization tools for performance analysis
  1. Hyperon Integration Planning:
  • Map transformer components to Hyperon's neural atomspace
  • Design data flow pathways between systems
  • Establish serialization protocols
  • Create integration test suites

Phase 2: Evolutionary Method Implementation

This phase systematically explores different evolutionary approaches, carefully documenting their characteristics:

  1. CMA-ES Implementation:
  • Begin with small feed-forward networks (100-1000 parameters)
  • Implement standard CMA-ES with detailed monitoring
  • Create adaptive population sizing mechanisms
  • Design hybrid systems where CMA-ES handles global optimization while gradients handle local refinement
  • Scale gradually to transformer architectures, carefully tracking computational requirements
  1. Architecture Evolution:
  • Implement encoding schemes for transformer architectures (attention heads, feed-forward layers, etc.)
  • Design specialized mutation operators for attention mechanisms
  • Create crossover operators that preserve architectural validity
  • Implement efficient fitness evaluation strategies
  • Develop repair mechanisms for invalid architectures
  1. Differential Evolution:
  • Implement quantum-inspired DE with specialized operators
  • Create adaptive parameter control mechanisms
  • Design hybrid strategies combining DE with backpropagation
  • Implement specialized crossover operators for neural network weights
  • Develop efficient population management strategies
  1. PSO Investigation:
  • Implement velocity-based updates for neural network parameters
  • Design neighborhood topologies suited for neural networks
  • Create adaptive inertia weight mechanisms
  • Implement multi-swarm strategies for different network components
  • Develop specialized position update rules for transformer architectures

Phase 3: Advanced Investigation

This phase explores sophisticated combinations and enhancements:

  1. Multi-Objective Optimization:
  • Implement NSGA-II with specialized genetic operators
  • Create MOEA/D framework for neural network optimization
  • Design objectives for model performance, size, and speed
  • Implement efficient non-dominated sorting
  • Create visualization tools for Pareto fronts

Phase 4: Scalability and Integration

This critical phase focuses on making our theoretical advances practical for real-world applications:

  1. Distributed Evolution: We'll implement parallel processing strategies specifically designed for evolutionary deep learning:
  • Design master-worker architectures where the master coordinates evolution while workers handle fitness evaluations
  • Develop efficient communication protocols to minimize overhead between distributed components
  • Create intelligent population distribution mechanisms that account for network topology and computational resources
  • Implement asynchronous evaluation strategies to maximize hardware utilization
  • Design fault tolerance mechanisms to handle worker failures gracefully
  1. Hyperon Integration: The integration with Hyperon's neural atomspace requires careful attention to both technical and practical considerations:
  • Develop clean, well-documented interfaces that follow Hyperon's architectural patterns
  • Create efficient serialization methods for evolutionary populations and neural networks
  • Implement caching mechanisms to avoid redundant computations
  • Design monitoring systems to track integration performance
  • Build comprehensive testing suites to ensure reliability

Phase 5: Validation and Analysis

This final phase provides rigorous validation of our findings and establishes theoretical foundations:

  1. Empirical Studies: We'll conduct extensive experiments to validate our approaches:
  • Design comprehensive test suites across different model scales
  • Create standardized comparison frameworks for fair evaluation against existing methods
  • Implement automated statistical analysis tools to ensure significance
  • Document edge cases and failure modes to understand limitations
  • Develop reproducibility guidelines for all experiments
  1. Theoretical Analysis: Understanding the theoretical foundations helps guide future research:
  • Analyze convergence properties through mathematical modeling
  • Study fitness landscape characteristics using visualization tools
  • Track population diversity metrics throughout evolution
  • Develop theoretical models explaining observed behaviors
  • Create mathematical frameworks for hybrid optimization

Phase 6: Documentation and Knowledge Transfer

Building on previous phases, we'll ensure our findings are accessible and usable:

  1. Technical Documentation:
  • Create detailed API documentation with usage examples
  • Develop comprehensive guides for implementing each evolutionary method
  • Write thorough installation and setup instructions
  • Provide troubleshooting guides and best practices
  1. Research Documentation:
  • Prepare detailed experimental protocols
  • Document all hyperparameter choices and their impacts
  • Create visualization tools for understanding results
  • Write clear explanations of theoretical findings
  1. Community Engagement:
  • Develop tutorials and workshops
  • Create example applications
  • Build interactive demonstrations
  • Write blog posts explaining key concepts

Throughout all phases, we maintain rigorous quality control through:

  • Regular code reviews using established criteria
  • Continuous integration testing
  • Performance benchmarking
  • Documentation updates
  • Weekly team meetings to discuss progress and challenges

 

Open Source Licensing

GNU GPL - GNU General Public License

Proposal Video

Not Avaliable Yet

Check back later during the Feedback & Selection period for the RFP that is proposal is applied to.

  • Total Milestones

    4

  • Total Budget

    $40,000 USD

  • Last Updated

    5 Dec 2024

Milestone 1 - Foundation and Baseline Establishment

Description

Establish the experimental foundation by implementing baseline transformer models with traditional training methods and setting up the core evaluation framework. This phase creates the benchmarks against which evolutionary methods will be compared.

Deliverables

Functional transformer implementation with standard training pipeline Comprehensive evaluation framework with automated testing Baseline performance metrics across selected datasets Initial integration points with Hyperon's neural atomspace

Budget

$8,000 USD

Success Criterion

Baseline models demonstrate performance within 5% of published results on standard benchmarks. Training convergence matches expected learning curves. Memory usage stays within predetermined budgets. All test suites execute successfully with 100% pass rate, Code coverage exceeds 85% for core components. Successful communication with Hyperon's neural atomspace demonstrated, Data serialization and deserialization working correctly, Basic operations verified through integration tests.

Milestone 2 - Evolutionary Methods Implementation

Description

Implement core evolutionary algorithms for neural network optimization.

Deliverables

Working implementations of CMA-ES DE and PSO. Comparative analysis of each method's performance. Documentation of all implemented algorithms.

Budget

$12,000 USD

Success Criterion

All planned evolutionary methods implemented and tested. Each algorithm demonstrates stable execution over multiple runs. Implementation matches theoretical specifications. At least one evolutionary method achieves parity with gradient descent baselines Computational overhead stays within 1.5x of traditional methods. Convergence reliability demonstrated across multiple random seeds. Modular implementation allowing easy algorithm comparison. Comprehensive test suite with >90% coverage. Clean integration with existing Hyperon components.

Milestone 3 - Advanced Optimization and Scaling

Description

Develop advanced optimization strategies and ensure scalability.

Deliverables

Implementation of NSGA-II and MOEA/D. Hybrid evolutionary-gradient descent approaches.

Budget

$12,000 USD

Success Criterion

Multi-objective optimization produces clear Pareto fronts. Hybrid approaches demonstrate superior performance versus pure methods. Solutions scale efficiently with problem size. Linear scaling up to predetermined problem sizes. Resource utilization remains within specified limits. Fault tolerance handles node failures gracefully. Seamless integration with Hyperon's distributed infrastructure. Reliable state management across distributed components. Clear performance monitoring and logging.

Milestone 4 - Validation and Final Integration

Description

Comprehensive validation and final integration completion.

Deliverables

Final performance analysis. Complete Hyperon integration. Comprehensive documentation.

Budget

$8,000 USD

Success Criterion

Statistical significance demonstrated for all key findings. Reproducibility verified by independent test runs. Edge cases and limitations clearly documented. Full functionality available through Hyperon's interfaces. All planned integration points implemented and tested. Performance overhead of integration layer <5%. Complete API documentation with usage examples. Comprehensive testing and deployment guides. Clear architectural documentation. Tutorial materials suitable for new users. At least one novel contribution identified and documented. Clear pathways for future research established. Potential applications in other domains identified.

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Expert Ratings

Reviews & Ratings

Group Expert Rating (Final)

Overall

4.9

  • Feasibility 5.0
  • Desirabilty 5.0
  • Usefulness 5.0

While experts originally rated this submission highly and argued in favor, ultimately we selected another proposal for strategic reasons.

  • Expert Review 1

    Overall

    5.0

    • Compliance with RFP requirements 5.0
    • Solution details and team expertise 5.0
    • Value for money 5.0
    Strong and complete

    Though the proposal could've been scoped better (PSO+DE, tight guarantees, overall ambitious wording for the budget) and the details on integration are left relatively high-level, it is technically clear and purposefully structured to address the RFP.

  • Expert Review 2

    Overall

    5.0

    • Compliance with RFP requirements 5.0
    • Solution details and team expertise 5.0
    • Value for money 5.0
    Solid proposal

    Hghly detailed and well-structured, with a clear focus on addressing fundamental questions about evolutionary methods in deep learning. Aligns closely with the RFP’s goals and demonstrates strong technical and scientific rigor. Overall, it is a strong candidate with high potential to advance both evolutionary learning and AGI frameworks.

  • Expert Review 3

    Overall

    5.0

    • Compliance with RFP requirements 5.0
    • Solution details and team expertise 5.0
    • Value for money 5.0
    Strong proposal with a decent amount of technical details

    Mixing CMA-ES for global search w/ gradients for local search is interesting and makes sense ... Generally this proposal stands out by inserting specific details that actually. make sense...

  • Expert Review 4

    Overall

    5.0

    • Compliance with RFP requirements 5.0
    • Solution details and team expertise 5.0
    • Value for money 5.0

    Ambitious, excellent and detailed proposal addressing and going beyond the RFP with a very detailed and systematic approach to studying a wide variety of processes including a CMA-ES Implementation, architecture evolution, quantum-inspired differential evolution, and swarm intelligence. Excellent breakdown of phases and tasks.

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