Publication detail

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

HRBÁČEK, R. SEKANINA, L.

Original Title

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

Type

conference paper

Language

English

Original Abstract

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.

Keywords

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

Authors

HRBÁČEK, R.; SEKANINA, L.

RIV year

2014

Released

12. 7. 2014

Publisher

Association for Computing Machinery

Location

New York

ISBN

978-1-4503-2662-9

Book

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

Pages from

1015

Pages to

1022

Pages count

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"
}