Genetic Programming with Transformer-Based Mutation for Approximate Circuit Design
Researchers have developed a new method for designing approximate arithmetic circuits using genetic programming enhanced by a transformer-based mutation operator. This hybrid approach aims to overcome stagnation in the evolutionary design process by integrating a standard mutation operator with the novel transformer-based one. The system was trained on a large dataset of genetic programming chromosomes representing approximate multipliers, and it has demonstrated the ability to achieve better trade-offs between error and performance compared to existing state-of-the-art libraries. AI
IMPACT Introduces a novel transformer-based mutation for genetic programming, potentially improving automated circuit design and leading to new, patentable designs.