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.

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

$8,000 USD

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.

Implement core evolutionary algorithms for neural network optimization.

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

$12,000 USD

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.

Develop advanced optimization strategies and ensure scalability.

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

$12,000 USD

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.

Comprehensive validation and final integration completion.

Final performance analysis. Complete Hyperon integration. Comprehensive documentation.

$8,000 USD

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.