Bloat-aware GP-based methods with bloat quantification
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{Liang:AI,
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author = "Jiayu Liang and Yu Xue",
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title = "Bloat-aware {GP}-based methods with bloat
quantification",
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journal = "Applied Intelligence",
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year = "2022",
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volume = "52",
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pages = "4211--4225",
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keywords = "genetic algorithms, genetic programming, bloat
quantification, Parsimony Pressure, Multi-objective
optimization",
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ISSN = "0924-669X",
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URL = "https://rdcu.be/cw94T",
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DOI = "doi:10.1007/s10489-021-02245-1",
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size = "15 pages",
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abstract = "Genetic programming (GP) solves optimization problems
by simulating the evolution procedure in nature. It has
a serious problem termed as bloat, which can cost
memory, hamper effective breeding and slow down the
evolution process. However, there are only a limited
number of works to quantify bloat directly, and
existing techniques use the solution size/complexity as
an indirect indicator for bloat control. Therefore, a
new bloat quantification measure is designed in this
work, based on which three bloat aware GP methods are
proposed. Specifically, the bloat quantification
measure is incorporated with two parsimony pressure
techniques and a multiobjective technique respectively,
termed as GPLTSb (GP Lexicographic Tournament Selection
bloat), GPPTSb (GP Proportional Tournament Selection
bloat), and MOGPb (Multi-objective GP bloat). Unlike
the existing bloat control methods, the bloat-aware
methods apply the bloat values directly for bloat
control. The proposed methods are tested on benchmark
symbolic regression tasks, and are compared with GP,
existing bloat control methods and four widely used
regression methods. Results show that MOGPb is
effective for bloat control with the solution size
reduced obviously; while GPLTSb and GPPTSb can also
reduce bloat in GP with the solution size reduced
slightly. In addition, compared with GP and existing
bloat control methods, the proposed methods evolve
solutions with similar/better regression performance.
Moreover, the evolved solutions of proposed methods can
outperform most reference regression methods for the
given tasks consistently.",
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notes = "Tianjin Key Laboratory of Autonomous Intelligent
Technology and System, Tiangong University, Tianjin
300387, China",
- }
Genetic Programming entries for
Jiayu Liang
Yu Xue
Citations