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This paper proposes Pitts-DNF-C, a multi- objective Pittsburgh-style Learning Classifier System that evolves a set of DNF-type fuzzy rules for classification tasks. The system is explicitly designed to only explore solutions that lead to consistent, complete, and compact rule sets without redundancies and inconsistencies. The behavior of the system is analyzed on a collection of real-world data sets,...
Accuracy in processing time estimation of manufacturing operations is fundamental to achieve more competitive prices and higher profits in an industry. The manufacturing times of a machine depend on several input variables and, for each class or type of product, a regression function for that machine can be defined. Time estimations are used for implementing production plans. These plans are usually...
In this paper we propose a color classification algorithm in which an evolutionary design optimizes a fuzzy system for color classification and image segmentation. This system works with the least number of rules and has minimum error rate by the mean of particle swarm optimization (PSO) method. In this approach each particle of the swarm codes a set of fuzzy rules. During evolution, each member of...
This work introduces the software tool KEEL to assess evolutionary algorithms for data mining problems including regression, classification, clustering, pattern mining and so on. It includes a big collection of genetic fuzzy system algorithms based on different approaches: Pittsburgh, Michigan, IRL and GCCL. It allows us to perform a complete analysis of any genetic fuzzy system in comparison to existing...
In this contribution, we present GP-COACH, a novel GFS based on the cooperative-competitive learning approach, that uses genetic programming to code fuzzy rules with a different number of variables, for getting compact and accurate rule bases for high dimensional problems. GP-COACH learns disjunctive normal form rules (generated by means of a context-free grammar) and uses a token competition mechanism...
In this paper, we first explain why many-objective problems are difficult for Pareto dominance-based evolutionary multiobjective optimization algorithms such as NSGA-II and SPEA. Then we explain recent proposals for the handling of many-objective problems by evolutionary algorithms. Some proposals are examined through computational experiments on multiobjective knapsack problems with two, four and...
Takagi-Sugeno-Kang-type fuzzy model to assist with real estate appraisals is described and optimized using evolutionary algorithms Two approaches were compared in the paper. The first one consisted in learning the rule base and the second one in combining learning the rule base and tuning the membership functions in one process. Five TSK-type fuzzy models comprising 3 or 4 input variables referring...
The main contribution of this paper is a novel fault detection strategy, which is able to cope with changing system states at on-line measurement systems fully automatically. For doing so, an improved fault detection logic is introduced which is based on data-driven evolving fuzzy models. These are sample-wise trained from online measurement data, i.e. the structure and rules of the models evolve...
An enhanced memory TSK-type recurrent fuzzy network (EM-TRFN) is proposed in this paper, for dynamic control of nonlinear systems. The network employs feedback connections in the rule layer, with their synaptic links being implemented through finite impulse response (FIR) filters. Thus, the network structure is enriched in terms of past information processing capabilities. Both structure and parameter...
In the field of self-optimizing automation systems, incremental local learning is an important technique. But especially in case of closed loop coupling, learnt anomalies may have a negative influence on the entire future of the evolving system. In the worst case, this may result in unstable or chaotic system behavior. Thus it is crucial to detect anomalies in online learning systems instantaneously...
Following the development of online markets, trading practices as dynamic pricing, online auctions and exchanges have become relevant to a variety of markets. In this paper we suggest a machine learning approach to find a suitable bidding strategy for an auction participant using information commonly available in online auction settings. We take the electricity auction as the main application example,...
This paper presents a genetic-fuzzy approach for hybrid electric vehicle control based on driving pattern recognition and prediction. In this approach, data collection in the real traffic conditions is employed for classification of several driving patterns. These driving patterns represent different traffic conditions e.g. congested, urban and so on. The analysis used for the driving pattern recognition...
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