Semantically-Oriented Mutation Operator in Cartesian Genetic Programming for Evolutionary Circuit Design
This addresses scalability issues in evolutionary circuit design for researchers, though it is incremental as it builds on existing semantic-aware methods in GP.
The paper tackled the limited scalability of Cartesian Genetic Programming (CGP) in evolutionary circuit design by proposing a semantically-oriented mutation operator (SOMO), which converged faster on Boolean benchmarks, evolving complex circuits like a 5x5-bit multiplier in less than an hour.
Despite many successful applications, Cartesian Genetic Programming (CGP) suffers from limited scalability, especially when used for evolutionary circuit design. Considering the multiplier design problem, for example, the 5x5-bit multiplier represents the most complex circuit evolved from a randomly generated initial population. The efficiency of CGP highly depends on the performance of the point mutation operator, however, this operator is purely stochastic. This contrasts with the recent developments in Genetic Programming (GP), where advanced informed approaches such as semantic-aware operators are incorporated to improve the search space exploration capability of GP. In this paper, we propose a semantically-oriented mutation operator (SOMO) suitable for the evolutionary design of combinational circuits. SOMO uses semantics to determine the best value for each mutated gene. Compared to the common CGP and its variants as well as the recent versions of Semantic GP, the proposed method converges on common Boolean benchmarks substantially faster while keeping the phenotype size relatively small. The successfully evolved instances presented in this paper include 10-bit parity, 10+10-bit adder and 5x5-bit multiplier. The most complex circuits were evolved in less than one hour with a single-thread implementation running on a common CPU.