Automated Algorithm Design

Goals

This project aims to revolutionize algorithm development by creating an automated framework that evolves hybrid algorithms outperforming existing methods. Using Multi-Objective Genetic Programming (MOGP), it combines advanced basis functions operating on vectors, matrices, images, and videos to design human-readable, competitive algorithms directly from data. MOGP generates a set of Pareto optimal solutions, allowing researchers to choose algorithms best suited to specific objectives and changing conditions. This approach frees researchers to guide optimization strategically and derive inspiration for new basis functions, fundamentally enhancing algorithm design in the era of big data and complex multi-objective challenges.

Issues Involved or Addressed

This project has many areas that should be investigated, including: improving the speed of evolutionary processes, integration of new basis functions from other domains, cloud computing, processing of big data sets, and application to the domains of interest.

Partners/Sponsors

Google