i. Literature Review and Planning: a. We will conduct a comprehensive literature review on evolutionary methods in neural networks. b. We will finalize the selection of datasets and models for our experiments. c. We will define evaluation metrics and establish detailed experimental protocols. ii. Implementation of EMs for Node Weight Optimization: a. We will implement evolutionary methods like CMA-ES, PSO, GWO, Firefly Algorithm, and ACO for optimizing neural network weights. b. We will begin initial experiments comparing these EMs with gradient-based methods on simple models such as MLPs and CNNs.

1. Literature Review Summary: A concise document summarizing key findings on evolutionary methods (CMA-ES, PSO, GWO, Firefly Algorithm, ACO) for neural network optimization and their comparison with gradient-based methods. 2. A finalized list of datasets (e.g., CIFAR-10, MNIST) and models (e.g., MLPs, CNNs) to be used. 3. Implementation of Evolutionary Methods for Node Weight Optimization: 4. Working implementations of selected evolutionary methods for optimizing weights in simple models. Code repository in google collab/github.

$15,000 USD

1. Literature Review and Dataset/Model Selection: Literature review document provides actionable insights, focusing on practical implementation needs. 2. Selected datasets and models are finalized and suitable for demonstrating the project's goals. 3. Functional Implementation: Evolutionary methods are successfully implemented and produce valid results during initial testing. Code is well-organized and ready for extension in future milestones. 4. Preliminary Results: Initial experiments generate basic performance metrics, showing functional comparisons between evolutionary methods and gradient-based optimization.

1. Implementation of EMs for DNN Architectures: Fully functional Python-based implementation of evolutionary methods (CMA-ES, PSO, GWO, Firefly Algorithm, ACO) for optimizing deep neural network architectures. Extended experiments with transformer models and larger architectures, demonstrating scalability. 2. Data Collection: Collected data on key metrics, including accuracy, convergence speed, computational cost, and scalability, for all experiments conducted. 3. Preliminary Statistical Analysis: Summary of trends and key findings from initial statistical evaluations of collected data, identifying areas for further investigation.

Implementation and Preliminary Analysis of the above

$10,000 USD

Evolutionary methods are successfully implemented and tested on simple and larger architectures, including transformers. Data collection is complete and relevant to the project goals. Preliminary analysis provides actionable insights, guiding further experimentation.

Integration with Hyperon: 1. Evolutionary methods implemented in Python are integrated into the Hyperon framework. 2. Evolutionary DNNs wrapped in Hyperon’s "Neural Atomspace" or equivalent module for compatibility. 3. Results comparing performance and functionality of evolutionary DNNs in standalone 4. Python implementation versus within the Hyperon framework.

Collaboration Documentation with above 3 points of milestones covered fully: Summary of modifications made for Hyperon compatibility along with code. Documentation of collaboration with SingularityNET/Hyperon team for guidance and feedback.

$5,000 USD

1. Integration is complete, and evolutionary methods operate successfully within Hyperon. 2. Functional testing validates the performance and reliability of methods in the Hyperon environment. 3. Comparative analysis highlights key differences, if any, between standalone and Hyperon-integrated versions.

Complete set of performance metrics from all experiments, including robustness tests. Detailed statistical evaluations with actionable insights.

1. Detailed Technical report summarizing methodology, experimental setups, results, and insights. 2. Fully functional demonstration of evolutionary DNNs within Hyperon, meeting all RFP requirements. 3. Complete codebase hosted in an open-source repository with clear documentation and licensing.

$10,000 USD

1. Final experiments deliver comprehensive insights into the performance and robustness of evolutionary methods. 2. Documentation is clear, concise, and supports easy replication by other researchers. 3. Hyperon integration meets all functionality and compatibility requirements, with a practical demonstration provided.