Detail publikace

Towards Highly Optimized Cartesian Genetic Programming: From Sequential via SIMD and Thread to Massive Parallel Implementation

HRBÁČEK, R. SEKANINA, L.

Originální název

Towards Highly Optimized Cartesian Genetic Programming: From Sequential via SIMD and Thread to Massive Parallel Implementation

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

Most implementations of Cartesian genetic programming (CGP) which can be found in the literature are sequential. However, solving complex design problems by means of genetic programming requires parallel implementations of search methods and fitness functions. This paper deals with the design of highly optimized implementations of CGP and their detailed evaluation in the task of evolutionary circuit design. Several sequential implementations of CGP have been analyzed and the effect of various additional optimizations has been investigated. Furthermore, the parallelism at the instruction, data, thread and process level has been applied in order to take advantage of modern processor architectures and computer clusters. Combinational adders and multipliers have been chosen to give a performance comparison with state of the art methods.

Klíčová slova

Cartesian Genetic Programming, Parallel Computing, SIMD, AVX, Cluster, Combinational Circuit Design

Autoři

HRBÁČEK, R.; SEKANINA, L.

Rok RIV

2014

Vydáno

12. 7. 2014

Nakladatel

Association for Computing Machinery

Místo

New York

ISBN

978-1-4503-2662-9

Kniha

GECCO '14 Proceedings of the 2014 conference on Genetic and evolutionary computation

Strany od

1015

Strany do

1022

Strany počet

8

URL

http://dl.acm.org/citation.cfm?id=2576768.2598343

BibTex

@inproceedings{BUT111521,
  author="Radek {Hrbáček} and Lukáš {Sekanina}",
  title="Towards Highly Optimized Cartesian Genetic Programming: From Sequential via SIMD and Thread to Massive Parallel Implementation",
  booktitle="GECCO '14 Proceedings of the 2014 conference on Genetic and evolutionary computation",
  year="2014",
  pages="1015--1022",
  publisher="Association for Computing Machinery",
  address="New York",
  doi="10.1145/2576768.2598343",
  isbn="978-1-4503-2662-9",
  url="http://dl.acm.org/citation.cfm?id=2576768.2598343"
}