Efficient Evolution of Parallel Binary Multipliers and Continuous Symbolic Regression Expressions with Sub-Machine-Code GP

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

@TechReport{poli:CSRP-98-19,

• author = "Riccardo Poli",
• title = "Efficient Evolution of Parallel Binary Multipliers and Continuous Symbolic Regression Expressions with Sub-Machine-Code {GP}",
• institution = "University of Birmingham, School of Computer Science",
• number = "CSRP-98-19",
• month = dec,
• year = "1998",
• file = "/1998/CSRP-98-19.ps.gz",
• URL = "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1998/CSRP-98-19.ps.gz",
• reportfilename = "pub/tech-reports/1998/CSRP-98-19.ps.gz",
• keywords = "genetic algorithms, genetic programming",
• abstract = "Sub-machine-code GP (SMCGP) is a new technique to speed up genetic programming (GP) and to extend its scope based on the idea of exploiting the internal parallelism of sequential CPUs. In previous work we have shown examples of applications of this technique to the evolution of parallel programs and to the parallel evaluation of 32 or 64 fitness cases per program execution in Boolean classification problems. After recalling the basic features of SMCGP, in this paper we first apply this technique to the problem of evolving parallel binary multipliers. Then we describe how SMCGP can be extended to process multiple fitness cases per program execution in continuous symbolic regression problems where inputs and outputs are real-valued numbers, reporting experimental results on a quartic polynomial approximation task.",

}

Genetic Programming entries for Riccardo Poli

Citations