Preference-driven Pareto front exploitation for bloat control in genetic programming
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- @Article{LIANG:2020:ASC,
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author = "Jiayu Liang and Yuxin Liu and Yu Xue",
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title = "Preference-driven {Pareto} front exploitation for
bloat control in genetic programming",
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journal = "Applied Soft Computing",
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year = "2020",
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volume = "92",
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pages = "106254",
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month = jul,
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keywords = "genetic algorithms, genetic programming, Preference,
Pareto front, Multi-objective optimization, Bloat
control",
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ISSN = "1568-4946",
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URL = "http://www.sciencedirect.com/science/article/pii/S1568494620301940",
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DOI = "doi:10.1016/j.asoc.2020.106254",
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size = "18 pages",
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abstract = "As one of evolutionary algorithms (EAs), genetic
programming (GP) has been applied in a wide range of
areas, e.g. bioinformatics and robotics. Different from
other EAs, GP can represent problems with variable
length (e.g. trees), which makes it more flexible in
evolving solutions, yet leads to a serious problem,
bloat. It can cause evolving redundant parts and
slowing down search. Multi-objective techniques are
popularly used for reducing bloat in GP (termed as
MOGP). Specifically, MOGP methods evolve trade-off
solutions of all objectives, which constitute the
so-called Pareto front. Then users select solutions on
the front based on their preference for specific tasks.
However, existing MOGP methods rarely consider users
preference during evolution, which wastes computation
power and time to search for useless solutions and
cannot generate fine-grained interested regions on the
Pareto front. Therefore, this paper investigates
introducing users' preference to guide multi-objective
techniques to focus on the interested regions on Pareto
front during evolution. Specifically, Pareto dominance
is an important notion in multi-objective techniques
for comparing two solutions. We design two
preference-driven Pareto dominance mechanisms, scPd
(static constraint Pareto dominance) and dcPd (dynamic
constraint Pareto dominance), which are introduced in a
base multi-objective technique and then are
incorporated with GP respectively to form two new bloat
control MOGP methods, i.e. scPd_MOGP and dcPd_MOGP.
They are tested on benchmark symbolic regression tasks
comparing with GP, two existing bloat control methods
(i.e. a parsimony GP method (pGP) and a standard
multi-objective GP method (sMOGP)), and four
popularly-used symbolic regression methods. Results
show that the proposed methods can reduce bloat in GP
and outperform pGP in bloat control, and comparison
with sMOGP shows that they can search front regions
based on users preference where the solutions have
better functionality, yet relatively larger sizes. In
addition, compared with four popularly-used symbolic
regression methods, scPd_MOGP is generally better;
while dcPd_MOGP achieves varied results, yet it
performs better or similar to the reference methods on
the majority of the given test functions. Moreover,
comparison between the two proposed methods suggests
that the constraint in the Pareto dominance of
scPd_MOGP is more relaxed than that of dcPd_MOGP",
- }
Genetic Programming entries for
Jiayu Liang
Yuxin Liu
Yu Xue
Citations