Genetic Programming Lifelong Multitasking Evolution: LLGP-Tasking
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Kattan:2023:SMC,
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author = "Ahmed Kattan and Faiyaz Doctor",
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booktitle = "2023 IEEE International Conference on Systems, Man,
and Cybernetics (SMC)",
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title = "Genetic Programming Lifelong Multitasking Evolution:
{LLGP-Tasking}",
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year = "2023",
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pages = "1403--1410",
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month = oct,
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keywords = "genetic algorithms, genetic programming, Training,
Sociology, Position measurement, Multitasking,
Extraterrestrial measurements, Task analysis",
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ISSN = "2577-1655",
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DOI = "doi:10.1109/SMC53992.2023.10393865",
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abstract = "We present a Lifelong Multi-Tasking learning algorithm
based on Genetic Programming referred to as
'LLGP-Tasking'. This paper extends previously published
work 'GP-Tasking' [7], evolving a population of GP
trees using a multi-faceted strategy. In GP-Tasking,
each individual is trained with multiple fitness
functions (where each function represents one task and
has different training/testing sets). Empirical
evidence demonstrated that the quality of evolved
solutions is comparable to standard GP achieving
significantly faster computational time while
maintaining smaller evolved population sizes. In this
work, we improved GP-Tasking allowing the system to
accumulate knowledge and use them not only in
multitasking, but also with different problems to mimic
lifelong learning. We further introduced a new
crossover mechanism to transfer useful knowledge across
different tasks. Moreover, we introduced new population
initialisation approach to accumulate knowledge across
different domains. Experimental results of the new
LLGP-Tasking demonstrate superiority of evolved
solutions over standard GP and it maintained same
search speed produced by its predecessor (i.e.,
GP-Tasking).",
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notes = "Also known as \cite{10393865}",
- }
Genetic Programming entries for
Ahmed Kattan
Faiyaz Doctor
Citations