The GA--P: A Genetic Algorithm and Genetic Programming hybrid

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{howard:1995:GA-P,
  author =       "Les M. Howard and Donna J. D'Angelo",
  title =        "The {GA--P}: A Genetic Algorithm and Genetic
                 Programming hybrid",
  journal =      "IEEE Expert",
  year =         "1995",
  volume =       "10",
  number =       "3",
  pages =        "11--15",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/64.393137",
  size =         "5 pages",
  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).",
  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.",
  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

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