Age-Fitness Pareto Optimization
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InCollection{Schmidt:2010:GPTP,
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author = "Michael Schmidt and Hod Lipson",
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title = "Age-Fitness Pareto Optimization",
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booktitle = "Genetic Programming Theory and Practice VIII",
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year = "2010",
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editor = "Rick Riolo and Trent McConaghy and
Ekaterina Vladislavleva",
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series = "Genetic and Evolutionary Computation",
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volume = "8",
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address = "Ann Arbor, USA",
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month = "20-22 " # may,
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publisher = "Springer",
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chapter = "8",
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pages = "129--146",
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keywords = "genetic algorithms, genetic programming, Symbolic
Regression, Age, Fitness, Multi-objective",
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isbn13 = "978-1-4419-7746-5",
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URL = "http://www.springer.com/computer/ai/book/978-1-4419-7746-5",
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DOI = "doi:10.1007/978-1-4419-7747-2_8",
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abstract = "We propose a multi-objective method, inspired by the
Age Layered Population Structure algorithm, for
avoiding premature convergence in evolutionary
algorithms, and demonstrate a three-fold performance
improvement over comparable methods. Previous research
has shown that partitioning an evolving population into
age groups can greatly improve the ability to identify
global optima and avoid converging to local optima.
Here, we propose that treating age as an explicit
optimization criterion can increase performance even
further, with fewer algorithm implementation
parameters. The proposed method evolves a population on
the two-dimensional Pareto front comprising (a) how
long the genotype has been in the population (age); and
(b) its performance (fitness). We compare this approach
with previous approaches on the Symbolic Regression
problem, sweeping the problem difficulty over a range
of solution complexities and number of variables. Our
results indicate that the multi-objective approach
identifies the exact target solution more often than
the age-layered population and standard population
methods. The multi-objective method also performs
better on higher complexity problems and higher
dimensional datasets - finding global optima with less
computational effort.",
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notes = "part of \cite{Riolo:2010:GPTP}",
- }
Genetic Programming entries for
Michael D Schmidt
Hod Lipson
Citations