Asynchronous Parallel Evolutionary Algorithms: Leveraging Heterogeneous Fitness Evaluation Times for Scalability and Elitist Parsimony Pressure

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

@InProceedings{Martin:2015:GECCOcompa,

• author = "Matthew A. Martin and Alex R. Bertels and Daniel R. Tauritz",
• title = "Asynchronous Parallel Evolutionary Algorithms: Leveraging Heterogeneous Fitness Evaluation Times for Scalability and Elitist Parsimony Pressure",
• booktitle = "GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation",
• year = "2015",
• editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr",
• isbn13 = "978-1-4503-3488-4",
• keywords = "genetic algorithms, genetic programming: Poster",
• pages = "1429--1430",
• month = "11-15 " # jul,
• organisation = "SIGEVO",
• address = "Madrid, Spain",
• URL = "http://doi.acm.org/10.1145/2739482.2764718",
• DOI = "doi:10.1145/2739482.2764718",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• abstract = "Many important problem classes lead to large variations in fitness evaluation times, such as is often the case in Genetic Programming where the time complexity of executing one individual may differ greatly from that of another. Asynchronous Parallel Evolutionary Algorithms (APEAs) omit the generational synchronization step of traditional EAs which work in well-defined cycles. This paper provides an empirical analysis of the scalability improvements obtained by applying APEAs to such problem classes, aside from the speed-up caused merely by the removal of the synchronization step. APEAs exhibit bias towards individuals with shorter fitness evaluation times, because they propagate faster. This paper demonstrates how this bias can be leveraged in order to provide a unique type of elitist parsimony pressure which rewards more efficient solutions with equal solution quality.",
• notes = "Also known as \cite{2764718} Distributed at GECCO-2015.",

}

Genetic Programming entries for Matthew A Martin Alex R Bertels Daniel R Tauritz

Citations