A guided genetic programming with attribute node activation encoding for resource constrained project scheduling problem
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gp-bibliography.bib Revision:1.7964
- @Article{CHEN:2023:swevo,
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author = "Haojie Chen and Xinyu Li and Liang Gao",
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title = "A guided genetic programming with attribute node
activation encoding for resource constrained project
scheduling problem",
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journal = "Swarm and Evolutionary Computation",
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volume = "83",
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pages = "101418",
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year = "2023",
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ISSN = "2210-6502",
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DOI = "doi:10.1016/j.swevo.2023.101418",
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URL = "https://www.sciencedirect.com/science/article/pii/S2210650223001918",
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keywords = "genetic algorithms, genetic programming, Resource
constrained project scheduling, Guided search,
Attribute Node activation encoding, Priority rule",
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abstract = "The large-scale characteristic and complex logic
between activities have made priority rules (PRs) are
more favoured in actual project scheduling, resulting
in the increasing attention of genetic programming (GP)
with automatically generating more effective PRs.
However, the limitations of encoding and numerous
random search operators in existing GPs not only affect
the effectiveness of evolved PRs, but also reduce their
interpretability. This paper proposes a novel
Hyper-Heuristic based Guided Genetic Programming with
Attribute Node Activation Encoding for resource
constrained project scheduling problem. Uniquely, the
proposed method transforms existing single class
feature activation encoding into attribute node
activation encoding for independently controlling each
attribute node, and develops an attribute importance
calculation method based on the frequency of attribute
occurrence and activation. Based on the importance of
subtrees and attributes, four guided and two random
local search operators are designed to obtain more
characteristic PRs. In addition, a two-stage evolution
framework that automatically switches stages through
iteration number is constructed to achieve performance
sampling and guided generation of PRs. Based on the
PSPLIB benchmark, although with fewer attribute inputs,
the proposed method can generate more effective PRs
with significantly better results compared to 12
existing PRs and PRs evolved from the two latest GPs in
all test subsets",
- }
Genetic Programming entries for
Haojie Chen
Xinyu Li
Liang Gao
Citations