The GA--P: A Genetic Algorithm and Genetic Programming hybrid
Created by W.Langdon from
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- @Article{howard:1995:GA-P,
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author = "Les M. Howard and Donna J. D'Angelo",
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title = "The {GA--P}: A Genetic Algorithm and Genetic
Programming hybrid",
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journal = "IEEE Expert",
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year = "1995",
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volume = "10",
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number = "3",
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pages = "11--15",
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month = jun,
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/64.393137",
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size = "5 pages",
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abstract = "The GA-P performs symbolic regression by combining the
traditional genetic algorithm's function optimization
strength with the genetic-programming paradigm to
evolve complex mathematical expressions capable of
handling numeric and symbolic data. This technique
should provide new insights into poorly understood data
relationships. Discovering relationships has been a
task troubling researchers since the dawn of modern
science. Discovering relationships between sets of data
is laborious and error prone, and it is highly subject
to researcher bias. Because many of today's research
problems are more complex than those of the past, it is
increasingly important that robust data analysis
methods be available to researchers. For a data
analysis method to be most useful, it must meet at
least three criteria: good predictive ability, insight
into the inner workings of the system being analyzed,
and unbiased results. Historically, researchers deduced
relationships solely by examining the data--a difficult
task if the relationship is complex, if many variables
are involved, or if the data are noisy (as often occurs
in real-world problems).",
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abstract = "Moreover, the examination is easily influenced by the
researcher's desires and expectations. Statistical
methods were among the first tools developed to help a
researcher find the relationships of observed facts.
Statistical methods are often based on such assumptions
as these: (1) the data are normally distributed, (2)
the equation relating the data is of a specific form
(for example, linear, quadratic, or polynomial), and
(3) the variables are independent. If the problem meets
these assumptions, statistics are a valuable tool for
providing static descriptors. But real-world problems
seldom meet these criteria. Neural networks, an
artificial intelligence technique, are not limited by
these assumptions. They serve as strong predictive
models that can uncover complex relationships, but they
give little insight into the underlying mechanisms that
describe a relationship. However, two other
nonstatistical AI techniques, genetic algorithms and
genetic programming, are more robust methods of
exploring complex solution spaces. Independently, they
have had some success at revealing the mechanisms
relating data items. Recently, genetic algorithms,
which use the principles of evolution through natural
selection to solve problems, have established
themselves as a powerful search and optimization
technique. Most GAs are linear (the structure of an
individual is a flat bit string). The basic GA proceeds
as follows: 1. Create a population of random
individuals, in which each individual represents a
possible solution to the problem at hand. 2. Evaluate
each individual's fitness--its ability to solve the
specified problem. 3. Select individual population
members to be parents. 4. Produce children by
recombining parent material via crossover and mutation,
and add them to the population. 5. Evaluate the
children's fitness. 6. Repeat steps 3-5 until a
solution with the desired fitness goal is obtained. GAs
have been used for everything from multiple-fault
diagnosis to medical-image registration. They have
shown themselves to be a superior tool for developing
rule-based systems, capable of gleaning knowledge from
data inaccessible to statistical methods. Goldberg
thoroughly discusses genetic algorithms and their use
as a problem-solving and function optimization
technique. Goldberg and Forrest give additional
examples. Although linear GAs are adept at developing
rule-based systems, they cannot develop equations. A
recent addition to the evolutionary domain is genetic
programming, which uses an evolutionary approach to
generate symbolic expressions and perform symbolic
regressions. However, the genetic-programming method of
performing symbolic regressions has some limitations.
It can modify only the structure of an expression, not
its contents, which is generated by the implementation
program when the genetic programming starts. In
performing symbolic regressions, genetic programming
cannot deal with nonnumeric variables. It also tends to
produce convoluted equations because it cannot modify
the coefficients it uses (for example, a genetic
program might use (2.523+2.523)/2.523 to represent the
number 2). We have developed a method combining the
known strengths of traditional genetic algorithms with
the new field of genetic programming to produce a
superior tool for performing symbolic regressions. We
call this tool the genetic algorithm-program, or the
GA-P.",
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notes = "University of Georgia. IEEE Expert Special Track on
Evolutionary Programming (P. J. Angeline editor)
\cite{angeline:1995:er}",
- }
Genetic Programming entries for
Les M Howard
Donna J D'Angelo
Citations