Genetic Programming Multitasking
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Kattan:2020:SSCI,
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author = "Ahmed Kattan and Faiyaz Doctor and Yew-Soon Ong and
Alexandros Agapitos",
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title = "Genetic Programming Multitasking",
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booktitle = "2020 IEEE Symposium Series on Computational
Intelligence (SSCI)",
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year = "2020",
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pages = "1004--1012",
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abstract = "In this paper, we present a new multitasking algorithm
for Genetic Programming (GP). Our proposed algorithm
(referred to as {"}GP-Tasking{"}) evolves population
using multifaceted strategy. Each individual is trained
with different training sets and evaluated with
multiple fitness functions (where each fitness function
represents one task). At the beginning of the run,
GP-Tasking, randomly uses crossover operator to
facilitate knowledge transfer between different tasks
and store probability of constructive crossover
operators between different tasks. This information is
used to bias the crossover between tasks that have
higher probability of producing fitter offspring. The
novelty of GP Tasking, is that it uses one population
in the same phenotype space but with different
interpretations to explore multiple genotype spaces.
GP-Tasking was evaluated with 3 sets of experiments
where in each set we tested GP-Tasking ability to solve
5 different tasks simultaneously. Results showed that
GPTasking evolved smaller solutions and consume
significantly less computational time.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/SSCI47803.2020.9308600",
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month = dec,
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notes = "Also known as \cite{9308600}",
- }
Genetic Programming entries for
Ahmed Kattan
Faiyaz Doctor
Yew-Soon Ong
Alexandros Agapitos
Citations