A. Gonzalez and F. Herrera, Multi-Stage Genetic Fuzzy Systems Based on the Iterative Rule Learning Approach, Mathware and Soft Computing Vol 4, pp. 233-249, 1997. Home/Search Document Details and Download
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Boosting a Genetic Fuzzy Classifier - Frank Hoffmann Royal (2001) (2 citations) (Correct)
....adapt Figure1.Architectureofboosted genetic fuzzy classi er. timization of fuzzy rule based classification systems (FRBCS) or short genetic fuzzy classifier [3, 10, 13] The evolutionary design method for FRBCS presented in this paper follows the iterative rule learning (IRL) approach [7]. The overall architecture of the proposed approach is depicted in figure 1. The rule base is built in an incremental fashion by repeatedly invoking a genetic fuzzy rule generation algorithm. The genetic fuzzy system identifies those fuzzy rules that best match and correctly classify the current ....
....that in the selection step of the genetic algorithm fuzzy rules compete with each other, whereas in the fuzzy inference step the overall output is aggregated of the actions proposed by multiple, simultaneously active fuzzy rules. The iterative rule learning (IRL) approach, first proposed in [7], addresses the competition versus cooperation problem in that it divides the learning algorithm into two stages, a generation stage that obtains a preliminary set of rules, and a second post processing stage which refines the obtained rules in order to improve the cooperation among them. The ....
A. Gonzalez and F. Herrera. Multi-stage genetic fuzzy systems based on the iterative rule learning approach. Mathware & Soft Computing, 4:233--249, 1997.
Boosting a Genetic Fuzzy Classifier - Hoffmann (2001) (2 citations) (Correct)
....adapt Figure 1. Architecture of boosted genetic fuzzy classi er. timization of fuzzy rule based classification systems (FRBCS) or short genetic fuzzy classifier [3, 10, 13] The evolutionary design method for FRBCS presented in this paper follows the iterative rule learning (IRL) approach [7]. The overall architecture of the proposed approach is depicted in figure 1. The rule base is built in an incremental fashion by repeatedly invoking a genetic fuzzy rule generation algorithm. The genetic fuzzy system identifies those fuzzy rules that best match and correctly classify the current ....
....that in the selection step of the genetic algorithm fuzzy rules compete with each other, whereas in the fuzzy inference step the overall output is aggregated of the actions proposed by multiple, simultaneously active fuzzy rules. The iterative rule learning (IRL) approach, first proposed in [7], addresses the competition versus cooperation problem in that it divides the learning algorithm into two stages, a generation stage that obtains a preliminary set of rules, and a second post processing stage which refines the obtained rules in order to improve the cooperation among them. The ....
A. Gonzalez and F. Herrera. Multi-stage genetic fuzzy systems based on the iterative rule learning approach. Mathware & Soft Computing, 4:233--249, 1997.
Evolutionary Approaches To The Learning Of Fuzzy.. - Cordón, Jesus, Herrera Self-citation (Herrera) (Correct)
.... be most useful in an on line, real time environment in which radical changes in behaviour cannot be tolerated, whereas the Pittsburgh approach is more useful for off line environments in which more leisurely exploration and more radical behavioural changes are acceptable [21] Gonzlez and Herrera [28] reported that the major problem with the Michigan approach is that of resolving the conflict between the individual and collective interests of rules within the system. The ultimate aim of a Classification System learning process is to obtain a set of co adapted rules which act together in ....
....the problem of explicit competition between rules, large amounts of computing resources are required to evaluate a complete population of RBs. 4. 3 The Iterative Rule Learning Approach In recent bibliographies, a new learning model based on GAs and named the iterative learning approach ( 14] [28], 30] and [75] appears as an alternative to the Michigan and Pittsburgh approaches. This new model considers, as does the Michigan approach, that each chromosome in the population represents a single rule, but contrary to the Michigan approach, only the best individual is considered as the ....
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Gonzlez, A., and Herrera, F. (1997), "Multi-stage genetic fuzzy systems based on the iterative rule learning approach," Mathware & Soft Computing, Vol. 4 (3), pp. 233-249.
Adaptation of Fuzzy Inference System Using Neural Learning - Abraham (Correct)
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A. Gonzalez and F. Herrera, Multi-Stage Genetic Fuzzy Systems Based on the Iterative Rule Learning Approach, Mathware and Soft Computing Vol 4, pp. 233-249, 1997.
Boosting a Genetic Fuzzy Classifier - Frank Hoffmann Royal (2001) (2 citations) (Correct)
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A. Gonzalez and F. Herrera. Multi-stage genetic fuzzy systems based on the iterative rule learning approach. Mathware & Soft Computing, 4:233--249, 1997.
Combining Boosting and Evolutionary Algorithms for.. - Frank Hoffmann Royal (Correct)
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A. Gonzalez, F. Herrera, Multi-stage genetic fuzzy systems based on the iterative rule learning approach, Mathware & Soft Computing 4 (1997) 233--249.
Discovering Numeric Association Rules via Evolutionary.. - Mata, Álvarez, Riquelme (2002) (Correct)
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Gonzalez, A., Herrera, F.: Multi-stage Genetic Fuzzy System Based on the Iterative Rule Learning Approach. Mathware & Soft Computing, 4 (1997)
An Evolutionary Algorithm to Discover Numeric Association.. - Mata, Alvarez, Riquelme (2002) (Correct)
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A. Gonzalez and F. Herrera. Multi-stage genetic fuzzy systems based on the iterative rule learning approach, 1997.
Learning Feed-Forward and Recurrent Fuzzy Systems: A.. - Surmann, Maniadakis (2001) (2 citations) (Correct)
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A. Gonzlez and F. Herrera, "Multi-stage genetic fuzzy systems based on the iterative rule learning approach," Mathware & Soft Computing, pp. 233--249, 1997.
An Innovative Genetic Fuzzy System For The.. - Satsios.. (Correct)
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A. Gonzalez, F. Herrera, "Multi-Stage Genetic Fuzzy Systems Based on the Iterative Rule Learning Approach," Mathware & Soft Computing, Vol.4, pp.233-249, 1997.
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