Biological Insights on Grammar-Structured Mutations Improve Fitness and Diversity
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{tiso:2023:GECCO,
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author = "Stefano Tiso and Pedro Carvalho and Nuno Lourenco and
Penousal Machado",
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title = "Biological Insights on {Grammar-Structured} Mutations
Improve Fitness and Diversity",
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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 = "558--567",
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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, mutation,
optimizers, grammar-guided genetic programming",
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isbn13 = "9798400701191",
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DOI = "doi:10.1145/3583131.3590472",
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size = "10 pages",
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abstract = "Grammar-Guided Genetic Programming (GGGP) employs a
variety of concepts from evolutionary theory to
autonomously design solutions for a given task. Recent
insights from evolutionary biology can lead to further
improvements in GGGP algorithms. In this paper, we
propose a new mutation approach called Facilitated
Mutation (FM) that is based on the theory of
Facilitated Variation. We evaluate the performance of
FM on the evolution of neural network optimizers for
image classification, a relevant task in Evolutionary
Computation, with important implications for the field
of Machine Learning. We compare FM and FM combined with
crossover (FMX) against a typical mutation approach to
assess the benefits of the approach. We find that FMX
provides statistical improvements in key metrics,
creating a superior optimizer overall (+0.5\% average
test accuracy), improving the average quality of
solutions (+53\% average population fitness), and
discovering more diverse high-quality behaviors (+523
high-quality solutions discovered on average).
Additionally, FM and FMX reduce the number of fitness
evaluations in an evolutionary run, reducing
computational costs. FM's implementation cost is
minimal and the approach is theoretically applicable to
any algorithm where genes are associated witha grammar
non-terminal, making this approach applicable in many
existing GGGP systems.",
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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
Stefano Tiso
Pedro Carvalho
Nuno Lourenco
Penousal Machado
Citations