A learning system based on genetic adaptive algorithms

By Stephen F. Smith. 1980

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Citations in the corpus

Genetic programming as a means for programming computers by natural selection (1994, methodology): “…the complexity of the structures undergoing adaptation using the genetic algorithm has been reflected by considerable work over the years in that direction [Smith 1980, Cramer 1985, Holland 1986, Holland et al. 1986, Wilson 1987a, Wilson 1987b, Fujiki and Dickinson 1987, Goldberg et al 1989].”
Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting (2010, methodology): “The number of linguistic terms forming each of them can be specified by the GFS designer, and then Pittsburgh approach [36] is used for learning rule base.”
A statistical approach to rule learning (2006, background): “Research on genetic algorithms for rule learning dates back to the 1980s (Smith, 1980; Holland, 1986).”
Genetic Programming: An Introduction (1997, background): “Smith published a significant dissertation on machine learning involving a classifier system that became known as “Smith’s Poker Player” (Smith, 1980).”
Ten years of genetic fuzzy systems: current framework and new trends (2001, methodology): “When considering a rule based system and focusing on learning rules, there are three main approaches that have been applied in the literature: Pittsburgh approach [61], Michigan approach [34] and Iterative rule learning approach [66].”
Learning Complexity-Bounded Rule-Based Classifiers by Combining. Association Analysis and Genetic Algorithms (2005, result): “For example, since a single chromosome in ClAss represents an entire rule base, it is to some extent comparable with the well-known Pittsburgh approach to learning fuzzy rule-based systems [13].”
Computational Intelligence and Feature Selection - Rough and Fuzzy Approaches (2008, background): “1 General Framework From some of the early work on classifier systems two terms have emerged that are still in common usage today: Michigan-style [134] and Pittsburgh-style [348], the names being in recognition of the institutions of the originators of the two approaches.”
Human-competitive results produced by genetic programming (2010, methodology): “In the mid-1980s, Nichael Cramer [20] described innovative experiments in program induction employing Smith’s crossover operation [18].”
Learning classifier systems: a complete introduction, review, and roadmap (2009, unknown): “Smith’s 1980 dissertation from the University of Pittsburgh [23] introduced LS-1, an alternative implementation that founded the fundamentally different “Pittsburgh-style” LCS.”
Global Optimization Algorithms -- Theory and Application (2009, result): “New results were reported by Smith [613] in his PhD thesis in 1980.”