Gene Permutation: A new Probabilistic Genetic Operator for Improving Multi Expression Programming
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- @InProceedings{Anjum:2019:SSCI,
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author = "Aftab Anjum and Mazharul Islam and Lin Wang",
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booktitle = "2019 IEEE Symposium Series on Computational
Intelligence (SSCI)",
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title = "Gene Permutation: A new Probabilistic Genetic Operator
for Improving Multi Expression Programming",
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year = "2019",
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pages = "3139--3146",
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month = dec,
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/SSCI44817.2019.9003048",
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abstract = "Multi-expression Programming (MEP) encodes multiple
genes through linear representation and is a widely
useful technique for tangible applications like
classification, symbolic regression and digital circuit
designing. MEP uses only two genetic operators
(mutation, crossover) to explore the search space and
exploit genetic materials. However, after going through
multiple generations and due to its naturally inspired
fitness-based selection procedure, MEP significantly
reduces genetic diversity in the population and
ultimately produces homogeneous individuals; hence,
leading to poor convergence and an ultimate fall into
the local minimum. Gene-permutation, the newly proposed
Probabilistic Genetic Operator, breakouts the
homogeneity by rearranging and inducing new genetic
materials in the individuals which in turn maintains
the healthy genetic diversity in the population.
Moreover, it also assists other genetic operators to
produce more effective chromosomes and fully explore
the search space. The experiments point out that
Gene-permutation improves training efficiency as well
as reduces test errors on several well-known symbolic
regression problems.",
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notes = "Also known as \cite{9003048}",
- }
Genetic Programming entries for
Aftab Anjum
Mazharul Islam
Lin Wang
Citations