Artificial evolution, fractal analysis and applications
Created by W.Langdon from
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- @Misc{oai:HAL:tel-02429815v1,
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author = "Pierrick Legrand",
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title = "Artificial evolution, fractal analysis and
applications",
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school = "Universite de Bordeaux",
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year = "2019",
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type = "l'habilitation a diriger des recherches",
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address = "UMR CNRS 5251, Bordeaux, France",
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month = "28 " # nov,
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keywords = "genetic algorithms, genetic programming",
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identifier = "tel-02429815",
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language = "en",
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oai = "oai:HAL:tel-02429815v1",
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rights = "info:eu-repo/semantics/OpenAccess",
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URL = "https://hal.inria.fr/tel-02429815/document",
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URL = "https://hal.inria.fr/tel-02429815/file/HDR_depot_HAL_20200108_biffe.pdf",
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size = "426 pages",
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abstract = "This document contains a selection of research works
to which I have contributed. It is structured around
two themes, artificial evolution and signal regularity
analysis and consists of three main parts: Part I:
Artificial evolution, Part II: Estimation of signal
regularity and Part III: Applications, combination of
signal processing, fractal analysis and artificial
evolution. In order to set the context and explain the
coherence of the rest of the document, this manuscript
begins with an introduction, Chapter 1, providing a
list of collaborators and of the research projects
carried out. Theoretical contributions focus on two
areas: evolutionary algorithms and the measurement of
signal regularity and are presented in Part I and Part
II respectively. These two themes are then exploited
and applied to real problems in Part III. Part I,
Artificial Evolution, consists of 8 chapters. Chapter 2
contains a brief presentation of various types of
evolutionary algorithms (genetic algorithms,
evolutionary strategies and genetic programming) and
presents some contributions in this area, which will be
detailed later in the document. Chapter 3, entitled
Prediction of Expected Performance for a Genetic
Programming Classifier proposes a method to predict the
expected performance for a genetic programming (GP)
classifier without having to run the program or sample
potential solutions in the research space. For a given
classification problem, a pre-processing step to
simplify the feature extraction process is proposed.
Then the step of extracting the characteristics of the
problem is performed. Finally, a PEP (prediction of
expected performance) model is used, which takes the
characteristics of the problem as input and produces
the predicted classification error on the test set as
output. To build the PEP model, a supervised learning
method with a GP is used. Then, to refine this work, an
approach using several PEP models is developed, each
now becoming a specialized predictors of expected
performance (SPEP) specialized for a particular group
of problems. It appears that the PEP and SPEP models
were able to accurately predict the performance of a
GP-classifier and that the SPEP approach gave the best
results. Chapter 4, entitled A comparison of
fitness-case sampling methods for genetic programming
presents an extensive comparative study of four
fitness-case sampling methods, namely: Interleaved
Sampling, Random Interleaved Sampling, Lexicase
Selection and the proposed Keep-Worst Interleaved
Sampling. The algorithms are compared on 11 symbolic
regression problems and 11 supervised classification
problems, using 10 synthetic benchmarks and 12
real-world datasets. They are evaluated based on test
performance, overfitting and average program size,
comparing them with a standard GP search. The
experimental results suggest that fitness-case sampling
methods are particularly useful for difficult
real-world symbolic regression problems, improving
performance, reducing overfitting and limiting code
growth. On the other hand, it seems that fitness-case
sampling cannot improve upon GP performance when
considering supervised binary classification. Chapter
5, entitled Evolving Genetic Programming Classifiers
with Novelty Search, deals with a new and unique
approach towards search and optimisation, the Novelty
Search (NS), where an explicit objective function is
replaced by a measure of solution novelty. This chapter
proposes a NS-based GP algorithm for supervised
classification. Results show that NS can solve
real-world classification tasks, the algorithm is
validated on real-world benchmarks for binary and
multiclass problems. Moreover, two new versions of the
NS algorithm are proposed, Probabilistic NS (PNS) and a
variant of Minimal Criteria NS (MCNS). The former
models the behaviour of each solution as a random
vector and eliminates all of the original NS parameters
while reducing the computational overhead of the NS
algorithm. The latter uses a standard objective
function to constrain and bias the search towards high
performance solutions. This chapter also discusses the
effects of NS on GP search dynam...",
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Genetic Programming entries for
Pierrick Legrand
Citations