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Participatory search is a population-based algorithm derived from the participatory learning paradigm. The algorithm accounts for the fact that the compatibility between individuals of the current population and the combination of compatibles, help to improve the value of an objective function during the search for an optimum. This paper focuses on the use of participatory search as a tool to develop...
This paper suggests a framework based on participatory learning and evolutionary computation to develop fuzzy models. Participatory evolutionary learning is an algorithm in which the population influences the fitness of individuals during evolution. The framework selects individuals for reproduction using the compatibility between the best and individuals randomly selected from the old and current...
Participatory evolution is a learning paradigm recently introduced in the realm of fuzzy system modeling and system optimization. The paradigm benefits from the concept of participatory learning, genetic algorithms and differential evolution. In this paper we address two distinct participatory evolutionary learning algorithms. The first combines participatory learning and the processing steps of differential...
Genetic Fuzzy Systems have been successfully used as a modeling approach for numerous applications. There is an increasing interest on how to construct fuzzy models for different types of complex systems such as highly nonlinear, large-scale, multiobjective, and high-dimensional systems. Current state of the art indicates the use of fast and scalable evolutionary algorithms in complex fuzzy modeling...
This paper introduces a new approach for evolving fuzzy modeling using tree structures. The model is a fuzzy linear regression tree whose topology can be continuously updated through a statistical model selection test. A fuzzy linear regression tree is a fuzzy tree with linear model in each leaf. An incremental learning algorithm approach evolves the tree replacing leaves with subtrees that improve...
This paper introduces an approach to develop evolving fuzzy rule-based models based on the idea of participatory learning. Participatory learning is a means to learn and revise beliefs based on what is already known or believed. Participatory learning naturally induces unsupervised dynamic fuzzy clustering algorithms and provides an effective alternative construct evolving functional fuzzy models...
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