Semantic schema theory for genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{Zojaji:2016:aplint,
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author = "Zahra Zojaji and Mohammad Mehdi Ebadzadeh",
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title = "Semantic schema theory for genetic programming",
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journal = "Applied Intelligence",
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year = "2016",
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volume = "44",
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number = "1",
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pages = "67--87",
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month = jan,
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keywords = "genetic algorithms, genetic programming, Schema
theory, Semantic building blocks, Mutual information",
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ISSN = "1573-7497",
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URL = "https://rdcu.be/cImfy",
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DOI = "doi:10.1007/s10489-015-0696-4",
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size = "21 pages",
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abstract = "Schema theory is the most well-known model of
evolutionary algorithms. Imitating from genetic
algorithms (GA), nearly all schemata defined for
genetic programming (GP) refer to a set of points in
the search space that share some syntactic
characteristics. In GP, syntactically similar
individuals do not necessarily have similar semantics.
The instances of a syntactic schema do not behave
similarly, hence the corresponding schema theory
becomes unreliable. Therefore, these theories have been
rarely used to improve the performance of GP. The main
objective of this study is to propose a schema theory
which could be a more realistic model for GP and could
be potentially employed for improving GP in practice.
To achieve this aim, the concept of semantic schema is
introduced. This schema partitions the search space
according to semantics of trees, regardless of their
syntactic variety. We interpret the semantics of a tree
in terms of the mutual information between its output
and the target. The semantic schema is characterized by
a set of semantic building blocks and their joint
probability distribution. After introducing the
semantic building blocks, an algorithm for finding them
in a given population is presented. An extraction
method that looks for the most significant schema of
the population is provided. Moreover, an exact
microscopic schema theorem is suggested that predicts
the expected number of schema samples in the next
generation. Experimental results demonstrate the
capability of the proposed schema definition in
representing the semantics of the schema instances. It
is also revealed that the semantic schema theorem
estimation is more realistic than previously defined
schemata.",
- }
Genetic Programming entries for
Zahra Zojaji
Mohammad Mehdi Ebadzadeh
Citations