The Struggle for Existence: Time, Memory and Bloat

Created by W.Langdon from gp-bibliography.bib Revision:1.7686

@InProceedings{stevenson:2023:GECCOcomp,

• author = "John Stevenson",
• title = "The Struggle for Existence: Time, Memory and Bloat",
• booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference",
• year = "2023",
• editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen",
• pages = "175--178",
• address = "Lisbon, Portugal",
• series = "GECCO '23",
• month = "15-19 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, population dynamics, agent-based modeling, linear genetic programming, evolutionary algorithms, bloat",
• isbn13 = "9798400701191",
• DOI = "doi:10.1145/3583133.3590725",
• size = "4 pages",
• abstract = "Combining a spatiotemporal, multi-agent based model of a foraging ecosystem with linear, genetically programmed rules for the agents' behaviors results in implicit, endogenous, objective functions and selection algorithms based on {"}natural selection{"}. Use of this implicit optimization of genetic programs for study of biological systems is tested by application to an artificial foraging ecosystem, and compared with established biological, ecological, and stochastic gene diffusion models. Limited program memory and execution time constraints emulate real-time and concurrent properties of physical and biological systems, and stress test the optimization algorithms. Relative fitness of the agents' programs and efficiency of the resultant populations as functions of these constraints gauge optimization effectiveness and efficiency. Novel solutions confirm the creativity of the optimization process and provide an unique opportunity to experimentally test the neutral code bloating hypotheses. Use of this implicit, endogenous, evolutionary optimization of spatially interacting, genetically programmed agents is thus shown to be novel, consistent with biological systems, and effective and efficient in discovering fit and novel solutions.",
• notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for John Stevenson

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