A Genetic Algorithm for Combinational Logic Circuit Synthesis Using Directed Graph Primitives.

We introduce functionality-cognizant Genetic Algorithms (GAs) and graph-based operators to tackle the challenging search landscape of combinational digital circuit design. We introduce a novel circuit representation that builds upon Cartesian Genetic Programming (CGP), a popular grid-based method for representing directed graphs of connected components. Leveraging this, we introduce an original crossover operator that accounts for circuit component functionality and connectivity, as opposed to CGP, which only considers positional information in the chromosome. Additionally, we propose an innovative set of mutation operators and demonstrate successful evolution of fully functional and minimally sized common digital circuits including a variety of binary encoders and adders. Following successful synthesis of a four-bit adder, we present a generalizable machine learning approach for multi-layered search and optimization problems.