Genetic Programming for Automatic Design of Parameter Adaptation in Dual-Population Differential Evolution
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @InProceedings{stanovov:2023:ECADA,
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author = "Vladimir Stanovov and Eugene Semenkin",
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title = "Genetic Programming for Automatic Design of Parameter
Adaptation in {Dual-Population} Differential
Evolution",
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booktitle = "13th Workshop on Evolutionary Computation for the
Automated Design of Algorithms",
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year = "2023",
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editor = "Daniel Tauritz and John Woodward and Emma Hart",
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pages = "1736--1743",
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address = "Lisbon, Portugal",
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series = "GECCO '23",
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month = "15-19 " # jul,
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organisation = "SIGEVO",
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publisher = "Association for Computing Machinery",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, parameter
adaptation, hyper-heuristic, differential evolution",
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isbn13 = "9798400701191",
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DOI = "doi:10.1145/3583133.3596310",
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size = "8 pages",
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abstract = "The parameter adaptation is one of the main problems
in many evolutionary algorithms, including differential
evolution. Instead of manual development of new
methods, a hyper-heuristic approach can be used, where
an algorithm is applied to search for parameter
adaptation scheme. In this study the symbolic
regression genetic programming is applied to design
parameter adaptation method for differential evolution
algorithm with two populations L-NTADE. Due to
algorithmic scheme different from popular L-SHADE, the
L-NTADE may require specific adaptation mechanisms.
Each solution in genetic programming consists of three
trees, which generate scaling factor values based on
current resource, success rate and current values in
the memory cells, containing scaling factor and
crossover rate. The training is performed on a set of
30 benchmark functions from CEC 2017 competition on
numerical optimization, and at every generation of
genetic programming new problem dimension,
computational resource, optima location and rotation
matrices are generated for every test function. The
testing is performed on two benchmarks, CEC 2017 and
CEC/GECCO 2022. The results comparison shows that the
automatically designed parameter adaptation heuristics
are capable of outperforming the success-history
adaptation in many cases, including high-dimensional
problems and problems with different computational
resource.",
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notes = "GECCO-2023 A Recombination of the 32nd International
Conference on Genetic Algorithms (ICGA) and the 28th
Annual Genetic Programming Conference (GP)",
- }
Genetic Programming entries for
Vladimir Stanovov
Eugene Semenkin
Citations