Semantically‑oriented mutation operator in cartesian genetic programming for evolutionary circuit design

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{Hodan:GPEM,

• author = "David Hodan and Vojtech Mrazek and Zdenek Vasicek",
• title = "Semantically‑oriented mutation operator in cartesian genetic programming for evolutionary circuit design",
• journal = "Genetic Programming and Evolvable Machines",
• year = "2021",
• volume = "22",
• number = "4",
• pages = "539--572",
• month = dec,
• note = "Special Issue: Highlights of Genetic Programming 2020 Events",
• keywords = "genetic algorithms, genetic programming, cartesian genetic programming, evolvable hardware, Semantic operator, Semantic mutation, Evolutionary circuit design",
• ISSN = "1389-2576",
• URL = "https://rdcu.be/cyKFV",
• DOI = "doi:10.1007/s10710-021-09416-6",
• size = "34 pages",
• abstract = "Cartesian genetic programming (CGP) represents the most efficient method for the evolution of digital circuits. Despite many successful applications, however, CGP suffers from limited scalability, especially when used for evolutionary circuit design, i.e. design of circuits from a randomly initialized population. Considering the multiplier design problem, for example, the 5 by 5-bit multiplier represents the most complex circuit designed by the evolution from scratch. 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. we propose a semantically-oriented mutation operator (SOMOk) suitable for the evolutionary design of combinational circuits. In contrast to standard point mutation modifying the values of the mutated genes randomly, the proposed operator uses semantics to determine the best value for each mutated gene. Compared to the common CGP and its variants, 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 5 by 5-bit multiplier. The most complex circuits were evolved in less than one hour with a single-thread implementation running on a common CPU.",
• notes = "Faculty of Information Technology, IT4Innovations Centre of Excellence, Brno University of Technology, Brno, Czech Republic",

}

Genetic Programming entries for David Hodan Vojtech Mrazek Zdenek Vasicek

Citations