The Struggle for Existence: Time, Memory and Bloat
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @InProceedings{stevenson:2023:GECCOcomp,
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author = "John Stevenson",
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title = "The Struggle for Existence: Time, Memory and Bloat",
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booktitle = "Proceedings of the 2023 Genetic and Evolutionary
Computation Conference",
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year = "2023",
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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",
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pages = "175--178",
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address = "Lisbon, Portugal",
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series = "GECCO '23",
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month = "15-19 " # jul,
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organisation = "SIGEVO",
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publisher = "Association for Computing Machinery",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, population
dynamics, agent-based modeling, linear genetic
programming, evolutionary algorithms, bloat",
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isbn13 = "9798400701191",
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DOI = "doi:10.1145/3583133.3590725",
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size = "4 pages",
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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.",
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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
Citations