Genetic Programming with Transformer-Based Mutation for Approximate Circuit Design
A recent trend is to leverage machine learning models to improve the evolutionary design and optimization process. We propose a novel transformer-based mutation operator for Cartesian genetic programming (CGP) for the automated design of approximate arithmetic circuits. We introduce a hybrid scheme for CGP in which the proposed mutation operator is switched with the standard mutation operator to prevent stagnation of the circuit approximation process. We also develop a new training scheme for the underlying transformer that utilizes training vectors composed of thousands of CGP chromosomes representing various approximate multipliers. For several target error constraints, the approximate ...
| Authors | Ondrej Galeta, Lukas Sekanina |
|---|---|
| Primary category | cs.NE |
| Published | 20 May 2026 |
| Last updated at source | 20 May 2026 |
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