Evolutionary Learning of a Fuzzy Controller for Mobile Robotics

Mucientes, Manuel; Moreno, David; Bugarín, Alberto; Barro, Senén

doi:10.1007/3-540-32400-3_24

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…The use of EAs in problem solving is a widespread practice. Problems such as image retrieval (Stejić et al 2007), the learning of controllers in robotics (Mucientes et al 2006) or the improvement of e-learning systems (Romero et al 2004) show their suitability as problem solvers in a wide range of scientific fields.…”

Section: Introductionmentioning

confidence: 99%

KEEL: a software tool to assess evolutionary algorithms for data mining problems

et al. 2008

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This paper introduces a software tool named KEEL which is a software tool to assess evolutionary algorithms for Data Mining problems of various kinds including as regression, classification, unsupervised learning, etc. It includes evolutionary learning algorithms based on different approaches: Pittsburgh, Michigan and IRL, as well as the integration of evolutionary learning techniques with different pre-processing techniques, allowing it to perform a complete analysis of any learning model in comparison to existing software tools. Moreover, KEEL has been designed with a double goal: research and educational.

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Section: Introductionmentioning

confidence: 99%

KEEL: a software tool to assess evolutionary algorithms for data mining problems

et al. 2008

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“…Although many approaches for learning membership functions were proposed (Cordón et al 2001;Mucientes et al 2006;Roubos and Setnes 2001;Setnes and Roubos 2000;Wang et al 2000), most of them were usually used for classification or control problems. For fuzzy mining problems, Kaya and Alhajj (2006) proposed a GA-based approach to derive a predefined number of membership functions for getting a maximum profit within an interval of user specified minimum support values.…”

Section: Introductionmentioning

confidence: 99%

A genetic-fuzzy mining approach for items with multiple minimum supports

et al. 2008

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Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. Mining association rules from transaction data is most commonly seen among the mining techniques. Most of the previous mining approaches set a single minimum support threshold for all the items and identify the relationships among transactions using binary values. In the past, we proposed a genetic-fuzzy data-mining algorithm for extracting both association rules and membership functions from quantitative transactions under a single minimum support. In real applications, different items may have different criteria to judge their importance. In this paper, we thus propose an algorithm which combines clustering, fuzzy and genetic concepts for extracting reasonable multiple minimum support values, membership functions and fuzzy association rules from quantitative transactions. It first uses the k-means clustering approach to gather similar items into groups. All items in the same cluster are considered to have similar characteristics and are assigned similar values for initializing a better population. Each chromosome is then evaluated by the criteria of requirement satisfaction and suitability of membership functions to estimate its fitness value. Experimental results also show the effectiveness and the efficiency of the proposed approach.

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Evolutionary Learning of a Fuzzy Controller for Mobile Robotics

Cited by 2 publications

References 9 publications

KEEL: a software tool to assess evolutionary algorithms for data mining problems

KEEL: a software tool to assess evolutionary algorithms for data mining problems

A genetic-fuzzy mining approach for items with multiple minimum supports

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