Semantically-Oriented Mutation Operator in Cartesian Genetic Programming for Evolutionary Circuit Design

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F20%3APU138613" target="_blank" >RIV/00216305:26230/20:PU138613 - isvavai.cz</a>

Result on the web

<a href="http://arxiv.org/abs/2004.11018" target="_blank" >http://arxiv.org/abs/2004.11018</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3377930.3390188" target="_blank" >10.1145/3377930.3390188</a>

Result language

angličtina

Original language name

Semantically-Oriented Mutation Operator in Cartesian Genetic Programming for Evolutionary Circuit Design

Original language description

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 5×5-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 5×5-bit multiplier. The most complex circuits were evolved in less than one hour with a single-thread implementation running on a common CPU.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/LQ1602" target="_blank" >LQ1602: IT4Innovations excellence in science</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

GECCO 2020 - Proceedings of the 2020 Genetic and Evolutionary Computation Conference

ISBN

978-1-4503-7128-5

ISSN

—

e-ISSN

—

Number of pages

9

Pages from-to

940-948

Publisher name

Association for Computing Machinery

Place of publication

Cancún

Event location

Cancun

Event date

Jul 8, 2020

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000605292300109