Full Inclusive Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{marchetti:2024:CEC,
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author = "Francesco Marchetti and Mauro Castelli and
Illya Bakurov and Leonardo Vanneschi",
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title = "Full Inclusive Genetic Programming",
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booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)",
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year = "2024",
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editor = "Bing Xue",
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address = "Yokohama, Japan",
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month = "30 " # jun # " - 5 " # jul,
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming, Phenotypes,
Sociology, Evolutionary computation, Benchmark testing,
Space exploration, Maintenance, Population's Diversity,
Symbolic Regression, PMLB Benchmarks, Population
Initialization",
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isbn13 = "979-8-3503-0837-2",
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DOI = "doi:10.1109/CEC60901.2024.10611808",
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abstract = "This manuscript presents an improved version of the
Inclusive Genetic Programming (IGP) algorithm. The IGP
was developed to promote and maintain the population's
genotypic diversity and showed superior performance
compared to standard Genetic Programming (GP). In this
work, two modifications to the IGP are proposed: first,
the diversity promotion and maintenance mechanism is
enhanced with information from the phenotype of the
individuals rather than only the genotype; second, the
Evolutionary Demes Despeciation Algorithm - V2
(EDDA-V2) is used to initialize the population. The
phenotype is considered to differentiate the
individuals also according to their behaviour rather
than only their structure, while EDDA-V2 is employed to
start the evolution with a simultaneously diverse and
fit population, contrary to traditional initialization
techniques. The algorithms incorporating these
improvements are called Full Inclusive Genetic
Programming (FIGP) and FIGP _E, respectively with and
without the EDDA-V2 initialization. The experimental
results, performed over eight benchmarks and
considering six algorithms, demonstrate the superior
performance of FIGP and FIGP _E in comparison to other
GP formulations. Moreover, the EDDA-V2 initialization
allows for a significant reduction of the computational
time.",
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notes = "also known as \cite{10611808}
WCCI 2024",
- }
Genetic Programming entries for
Francesco Marchetti
Mauro Castelli
Illya Bakurov
Leonardo Vanneschi
Citations