SonOpt: understanding the behaviour of bi-objective population-based optimisation algorithms through sound

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Asonitis:2023:GPEM,

• author = "Tasos Asonitis and Richard Allmendinger and Matt Benatan and Ricardo Climent",
• title = "{SonOpt}: understanding the behaviour of bi-objective population-based optimisation algorithms through sound",
• journal = "Genetic Programming and Evolvable Machines",
• year = "2023",
• volume = "24",
• pages = "article no. 3",
• note = "Special Issue: Evolutionary Computation in Art, Music and Design",
• note = "Online first",
• keywords = "genetic algorithms, genetic programming, Sonification, Multi-objective optimisation, Population-based optimisation algorithms, Algorithm behaviour, Hypervolume, Sound",
• ISSN = "1389-2576",
• URL = "https://rdcu.be/c7KTf",
• DOI = "doi:10.1007/s10710-023-09451-5",
• code_url = "https://github.com/tasos-a/SonOpt-2.0",
• size = "41 pages",
• abstract = "We present an extension of SonOpt, the first ever openly available tool for the sonification of bi-objective population-based optimisation algorithms. SonOpt has already introduced benefits on the understanding of algorithmic behaviour by proposing the use of sound as a medium for the process monitoring of bi-objective optimisation algorithms. The first edition of SonOpt used two different sonification paths to provide information on convergence, population diversity, recurrence of objective values across consecutive generations and the shape of the approximation set. The present extension provides further insight through the introduction of a third sonification path, which involves hypervolume contributions to facilitate the understanding of the relative importance of non-dominated solutions. Using a different sound generation approach than the existing ones, this newly proposed sonification path",

}

Genetic Programming entries for Tasos Asonitis Richard Allmendinger Matt Benatan Ricardo Climent

Citations