Genetic Programming Lifelong Multitasking Evolution: LLGP-Tasking

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@InProceedings{Kattan:2023:SMC,

• author = "Ahmed Kattan and Faiyaz Doctor",
• booktitle = "2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)",
• title = "Genetic Programming Lifelong Multitasking Evolution: {LLGP-Tasking}",
• year = "2023",
• pages = "1403--1410",
• month = oct,
• keywords = "genetic algorithms, genetic programming, Training, Sociology, Position measurement, Multitasking, Extraterrestrial measurements, Task analysis",
• ISSN = "2577-1655",
• DOI = "doi:10.1109/SMC53992.2023.10393865",
• 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).",
• notes = "Also known as \cite{10393865}",

}

Genetic Programming entries for Ahmed Kattan Faiyaz Doctor

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