Genetic Programming Based on Novelty Search
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @PhdThesis{naredo:tel-01668776,
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author = "Enrique Naredo",
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title = "Genetic Programming Based on Novelty Search",
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school = "ITT, Instituto tecnologico de Tijuana",
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year = "2016",
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address = "Mexico",
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month = "3 " # jun,
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keywords = "genetic algorithms, genetic programming, novelty
search, classification, deception, bloat",
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hal_id = "tel-01668776",
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hal_version = "v1",
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bibsource = "OAI-PMH server at api.archives-ouvertes.fr",
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language = "en",
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oai = "oai:HAL:tel-01668776v1",
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rights = "info:eu-repo/semantics/OpenAccess",
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URL = "https://hal.inria.fr/tel-01668776/document",
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URL = "https://hal.inria.fr/tel-01668776",
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URL = "https://hal.inria.fr/tel-01668776/file/NaredoFINALThesis.pdf",
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size = "233 pages",
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abstract = "Novelty Search (NS) is a unique approach towards
search and optimisation,where an explicit objective
function is replaced by a measure of solution novelty.
However, NS has been mostly used in evolutionary
robotics, its usefulness in classic machine learning
problems has been unexplored. This thesis presents a
NS-based Genetic Programming(GP) algorithms for common
machine learning problems, with the following
contributions. It is shown that NS can solve real-world
classification,clustering and symbolic regression
tasks, validated on real world benchmarks and synthetic
problems. These results are made possible by using a
domain-specific behaviour descriptor, related to the
concept of semantics in GP. 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 the 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. The thesis also discusses the
effects of NS on GP search dynamics and code growth.
Results show that NS can be used as a realistic
alternative for machine learning, and particularly for
GP-based classification.",
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notes = "Also known as \cite{oai:HAL:tel-01668776v1}",
- }
Genetic Programming entries for
Enrique Naredo
Citations