Towards highly optimized cartesian genetic programming: from sequential via SIMD and thread to massive parallel implementation

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

@InProceedings{Hrbacek:2014:GECCO,

• author = "Radek Hrbacek and Lukas 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",
• editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges",
• isbn13 = "978-1-4503-2662-9",
• pages = "1015--1022",
• keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Parallel Computing, SIMD, AVX, Cluster, Combinational Circuit Design",
• month = "12-16 " # jul,
• organisation = "SIGEVO",
• address = "Vancouver, BC, Canada",
• URL = "https://www.fit.vut.cz/research/publication/10512",
• URL = "https://www.fit.vut.cz/research/publication-file/10512/p1015-hrbacek.pdf",
• URL = "http://doi.acm.org/10.1145/2576768.2598343",
• DOI = "doi:10.1145/2576768.2598343",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• size = "8 pages",
• 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.",
• notes = "Included in \cite{Hrbacek:thesis}

  p1016 '128 or even 256 test vectors ... SSE or AVX'

  POPCNT. Compiled chromosome. combinational adder design, combinational multiplier. function set: {BUF, NOT, AND, OR, XOR, NAND, NOR, XNOR}. mutation, no crossover. 10000 generations, or 100000 generations (pop=5).

  p1019 '2 x 8-core Intel E5-2670, 128 GB RAM, 2 x 600 GB 15 k scratch hard disks, connected by gigabit Ethernet and Infiniband links'

  p1021 'massively parallel CGP exploiting tens of islands'

  Also known as \cite{2598343} \cite{FITPUB10512} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)",

}

Genetic Programming entries for Radek Hrbacek Lukas Sekanina

Citations