A survey on dynamic populations in bio-inspired algorithms

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

@Article{Farinati:2024:GPEM,

• author = "Davide Farinati and Leonardo Vanneschi",
• title = "A survey on dynamic populations in bio-inspired algorithms",
• journal = "Genetic Programming and Evolvable Machines",
• year = "2024",
• volume = "25",
• pages = "Article no 19",
• note = "Online first",
• keywords = "genetic algorithms, genetic programming, Population-based algorithms, Bio-inspired algorithms, Population size, Dynamic population, Adaptive population",
• ISSN = "1389-2576",
• URL = "https://rdcu.be/dOTDV",
• DOI = "doi:10.1007/s10710-024-09492-4",
• size = "32 pages",
• abstract = "... a significant amount of computational effort in phases where the evolution is functioning well, and therefore a large population is not needed. This allows for reusing the previously saved computational effort when optimization becomes more challenging, and hence a greater computational effort is required. Numerous past contributions have demonstrated a notable advantage of using dynamically sized populations, often resulting in comparable results to those obtained by the standard PBBIAs but with a significant saving of computational effort. However, despite the numerous successes that have been presented, to date, there is still no comprehensive collection of past contributions on the use of dynamic populations that allows for their categorization and critical analysis. This article aims to bridge this gap by presenting a systematic literature review regarding the use of dynamic populations in PBBIAs, as well as identifying gaps in the research that can lead the path to future works.",
• notes = "NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312, Lisboa, Portugal",

}

Genetic Programming entries for Davide Farinati Leonardo Vanneschi

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