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Outliers are observations that lie far away from the fitting function deduced from the bulk of a set of observations. The outlier detection has become more challenging when the nature of data has involved with the “concept drifting.” To address this challenging issue, this study explores a decision support mechanism (DSM) for coping with the outlier detection problem in the concept drifting environment...
This paper proposes a hybrid neuro-evolutive algorithm (NEA) that uses a compact indirect encoding scheme (IES) for representing its genotypes, moreover has the ability to reuse the genotypes and automatically build modular, hierarchical and recurrent neural networks. A genetic algorithm (GA) evolves a Lindenmayer System that is used to design the neural network's architecture. This basic neural codification...
Efforts have been made in financial markets to deal with price movement predicting. Recent studies have shown that the market can be outperformed by methodologies with the aid of science. In other words, it has been shown that methods based on computational intelligence can be more profitable than a buy-and-hold strategy. This paper proposes a probabilistic and dynamic chart pattern recognition hybrid...
Following analyzing existing challenges in addressing the balance between exploration and exploitation encountered by evolutionary algorithms, this paper develops a Genetic Algorithm with speciation (GASP). It first incorporates a novel encoding scheme and recombination method for a balanced genetic divergence when locating global optima in complex applications, such as structural and dynamic design...
Recently, performance of deep neural networks, especially convolutional neural networks (CNNs), has been drastically increased by elaborate network architectures, by new learning methods, and by GPU-based high-performance computation. However, there are still several difficult problems concerning back propagation, which include scheduling of learning rate and controlling locality of search (i.e.,...
Feature selection, instance selection and semi-supervised clustering are different challenges for machine learning and data mining communities. While other works have addressed each of these problems separately, in this paper we show how they can be addressed together, simultaneously. We propose an unified framework for semi-supervised co-selection of features and instances, based on weighting constrained...
Advanced Driving Assitance Systems (ADAS) cover a wide range of systems that aim to provide increasingly a safe and efficient driving. Many of these systems are endowed with some intelligent skills which are, in many cases, addressed by means of Soft Computing (SC) paradigms like Neural Networks (NN) or fuzzy systems among others. However, SC algorithms require normally large computational resources...
In recent decades, the redundancy allocation problem (RAP) is becoming an increasingly important issue in the initial stages of planning, designing, and controlling of systems. However, RAP in multi-state system (MSSs) still has some restrictions that components have only two performances: perfect functionality and complete failure. Therefore, this paper formulates a new kind of RAP called repairable...
This paper describes two new algorithms for optimising the structure of trained Evolving Connectionist System (ECoS) artificial neural networks (ANN). It also presents the results of preliminary empirical evaluations of the algorithms. While ECoS are fast and efficient constructive ANN algorithms they can lose efficiency if they are allowed to grow too large. The algorithms presented in this paper...
This paper demonstrates the use of a classical Case-Based Reasoning (CBR) approach applied to the automatic train conduction scenario. We use a CBR model, where the adaptation task consists on a multi-objective optimization approach. To realize the case study we have used a train simulator. It is capable of conducting a train in a pre-defined railway providing relevant data about the conduction, such...
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