An Effective Many-Objective Extension of Genetic Programming and Its Benchmark
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- @InProceedings{Ohki:2019:DDP,
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author = "Makoto Ohki",
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title = "An Effective Many-Objective Extension of Genetic
Programming and Its Benchmark",
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booktitle = "2019 First International Conference on Digital Data
Processing (DDP)",
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year = "2019",
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pages = "92--96",
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month = nov,
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keywords = "genetic algorithms, genetic programming",
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DOI = "
doi:10.1109/DDP.2019.00027",
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abstract = "This paper describes many-objective extension (MaO
extension) of GP problem. The extension reduces a bloat
phenomena what often happens in GPs. MaO extension of
GP consists of addition of objective functions, a
partial sampling (PS) operator, a structural distance,
elimination of identical trees and subset size
scheduling. In this paper, MaO extension of GP are
applied to NSGA-II and SPEA2. Because few researchers
have considered effective benchmark problems for
many-objective genetic programming (MaOGP), MaOGP is
less developed than the many-objective evolutionary
algorithm (MaOEA). This paper proposes a new benchmark
problem for MaOGP. The benchmark problem is based on
the many-objective knapsack problem (MaOKSP). In the
benchmark, a function given by GP returns a binary
vector for MaOKSP. A research on such benchmark
problems contributes to the future development of
MaOGPs.",
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notes = "Also known as \cite{8948757}",
- }
Genetic Programming entries for
Makoto Ohki
Citations