Self-adaptive genetically programmed differential evolution
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Roy:2012:ICECE,
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author = "Pravakar Roy and Md. Jahidul Islam and
Md. Monirul Islam",
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booktitle = "7th International Conference on Electrical Computer
Engineering (ICECE 2012)",
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title = "Self-adaptive genetically programmed differential
evolution",
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year = "2012",
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pages = "639--642",
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address = "Dhaka, Bangladesh",
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month = "20-22 " # dec,
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isbn13 = "978-1-4673-1434-3",
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DOI = "doi:10.1109/ICECE.2012.6471631",
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abstract = "Differential evolution (DE) is a simple and efficient
technique for real parameter optimisation over
continuous spaces. Its success is highly dependent on
the choice of correct trial vector generation
strategies and control parameters. Choosing appropriate
trial vector generation strategies and control
parameters for new problems by trial and error method
can be computationally costly and inefficient. This
paper proposes a hybrid approach, incorporating genetic
programming (GP) with DE, where GP generates trial
vector generation strategies based on the problem
specification and the state of the population using a
simple learning method. Trial vector generation
strategies are chosen from this pool of strategies
generated by GP. The choice of a particular strategy
depends on the type of the problem, initialisation
values and state of evolution. Consequently, the
strategies chosen for different run of the same problem
are different. However, it allows self-adaptation to be
completely problem dependent and as a result for a
unknown problem domain the method is expected to
perform better than other state-of-the-art
self-adaptive evolutionary techniques. In this method,
the control parameter F is eliminated and crossover
ratio Cr is evolved with the population and population
size NP is still fixed empirically. The performance of
this method is extensively evaluated using the CEC2005
contest test instances. Experimental results show that,
self-adaptive genetically programmed differential
evolution (SaGPDE) leads to quick convergence and
produce very competitive results.",
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keywords = "genetic algorithms, genetic programming, differential
equations, learning (artificial intelligence), CEC2005
contest test instances, SaGPDE, continuous spaces,
control parameters, real parameter optimisation,
self-adaptive evolutionary techniques, self-adaptive
genetically programmed differential evolution, simple
learning method, trial and error method, trial vector
generation strategies, Erbium, Evolutionary
computation, Optimisation, Sociology, Statistics,
Vectors, Differential evolution, self-adaptation, trial
vector generation strategy",
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notes = "Also known as \cite{6471631}",
- }
Genetic Programming entries for
Pravakar Roy
Md Jahidul Islam
Md Monirul Islam
Citations