70,783 research outputs found
Feature selection using genetic algorithms and probabilistic neural networks
Selection of input variables is a key stage in building
predictive models, and an important form of data mining. As exhaustive evaluation of potential input sets using full non-linear models is impractical, it is necessary to use simple fast-evaluating models and heuristic selection strategies. This paper discusses a fast, efficient, and powerful nonlinear input selection procedure using a combination of Probabilistic Neural Networks and repeated
bitwise gradient descent. The algorithm is compared
with forward elimination, backward elimination and genetic algorithms using a selection of real-world data sets. The algorithm has comparative performance and greatly reduced execution time with respect to these alternative approaches. It is demonstrated empirically that reliable results cannot be gained using any of these approaches without the use of resampling
Use of genetic algorithms and gradient based optimization techniques for calcium phosphate precipitation
Phase equilibrium computations constitute an important problem for designing and optimizing crystallization processes. The Gibbs free
energy is generally used as an objective function to find phase amount and composition at equilibrium. In such problems, the Gibbs free
energy may be a quite complex function, with several local minima. This paper presents a contribution to handle this kind of problems by
implementation of an optimization technique based on the successive use of a genetic algorithm (GA) and of a classical sequential quadratic
programming (SQP) method: the GA is used to perform a preliminary search in the solution space for locating the neighborhood of the
solution. Then, the SQP method is employed to refine the best solution provided by the GA. The basic operations involved in the design of
the GA developed in this study (encoding with binary representation of real values, evaluation function, adaptive plan) are presented. Several
test problems are first presented to demonstrate the validity of the approach. Then, calcium phosphate precipitation which is of major interest
for P-recovery from wastewater, has been chosen as an illustration of the implemented algorithm
Generative Adversarial Networks Selection Approach for Extremely Imbalanced Fault Diagnosis of Reciprocating Machinery
At present, countless approaches to fault diagnosis in reciprocating machines have been proposed, all considering that the available machinery dataset is in equal proportions for all conditions. However, when the application is closer to reality, the problem of data imbalance is increasingly evident. In this paper, we propose a method for the creation of diagnoses that consider an extreme imbalance in the available data. Our approach first processes the vibration signals of the machine using a wavelet packet transform-based feature-extraction stage. Then, improved generative models are obtained with a dissimilarity-based model selection to artificially balance the dataset. Finally, a Random Forest classifier is created to address the diagnostic task. This methodology provides a considerable improvement with 99% of data imbalance over other approaches reported in the literature, showing performance similar to that obtained with a balanced set of data.National Natural Science Foundation of China, under Grant 51605406National Natural Science Foundation of China under Grant 7180104
Evolving a rule system controller for automatic driving in a car racing competition
IEEE Symposium on Computational Intelligence and Games. Perth, Australia, 15-18 December 2008.The techniques and the technologies supporting Automatic Vehicle Guidance are important issues. Automobile manufacturers view automatic driving as a very interesting
product with motivating key features which allow improvement of the car safety, reduction in emission or fuel consumption or
optimization of driver comfort during long journeys. Car racing is an active research field where new advances in aerodynamics,
consumption and engine power are critical each season. Our proposal is to research how evolutionary computation techniques can help in this field. For this work we have designed an automatic controller that learns rules with a genetic algorithm.
This paper is a report of the results obtained by this controller during the car racing competition held in Hong Kong during the IEEE World Congress on Computational Intelligence (WCCI 2008).Publicad
Complex genome evolution in Anopheles coluzzii associated with increased insecticide usage in Mali.
In certain cases, a species may have access to important genetic variation present in a related species via adaptive introgression. These novel alleles may interact with their new genetic background, resulting in unexpected phenotypes. In this study, we describe a selective sweep on standing variation on the X chromosome in the mosquito Anopheles coluzzii, a principal malaria vector in West Africa. This event may have been influenced by the recent adaptive introgression of the insecticide resistance gene known as kdr from the sister species Anopheles gambiae. Individuals carrying both kdr and a nearly fixed X-linked haplotype, encompassing at least four genes including the P450 gene CYP9K1 and the cuticular protein CPR125, have rapidly increased in relative frequency. In parallel, a reproductively isolated insecticide-susceptible A. gambiae population (Bamako form) has been driven to local extinction, likely due to strong selection from increased insecticide-treated bed net usage
Predictive modeling of die filling of the pharmaceutical granules using the flexible neural tree
In this work, a computational intelligence (CI) technique named flexible neural tree (FNT) was developed to predict die filling performance of pharmaceutical granules and to identify significant die filling process variables. FNT resembles feedforward neural network, which creates a tree-like structure by using genetic programming. To improve accuracy, FNT parameters were optimized by using differential evolution algorithm. The performance of the FNT-based CI model was evaluated and compared with other CI techniques: multilayer perceptron, Gaussian process regression, and reduced error pruning tree. The accuracy of the CI model was evaluated experimentally using die filling as a case study. The die filling experiments were performed using a model shoe system and three different grades of microcrystalline cellulose (MCC) powders (MCC PH 101, MCC PH 102, and MCC DG). The feed powders were roll-compacted and milled into granules. The granules were then sieved into samples of various size classes. The mass of granules deposited into the die at different shoe speeds was measured. From these experiments, a dataset consisting true density, mean diameter (d50), granule size, and shoe speed as the inputs and the deposited mass as the output was generated. Cross-validation (CV) methods such as 10FCV and 5x2FCV were applied to develop and to validate the predictive models. It was found that the FNT-based CI model (for both CV methods) performed much better than other CI models. Additionally, it was observed that process variables such as the granule size and the shoe speed had a higher impact on the predictability than that of the powder property such as d50. Furthermore, validation of model prediction with experimental data showed that the die filling behavior of coarse granules could be better predicted than that of fine granules
How Noisy Data Affects Geometric Semantic Genetic Programming
Noise is a consequence of acquiring and pre-processing data from the
environment, and shows fluctuations from different sources---e.g., from
sensors, signal processing technology or even human error. As a machine
learning technique, Genetic Programming (GP) is not immune to this problem,
which the field has frequently addressed. Recently, Geometric Semantic Genetic
Programming (GSGP), a semantic-aware branch of GP, has shown robustness and
high generalization capability. Researchers believe these characteristics may
be associated with a lower sensibility to noisy data. However, there is no
systematic study on this matter. This paper performs a deep analysis of the
GSGP performance over the presence of noise. Using 15 synthetic datasets where
noise can be controlled, we added different ratios of noise to the data and
compared the results obtained with those of a canonical GP. The results show
that, as we increase the percentage of noisy instances, the generalization
performance degradation is more pronounced in GSGP than GP. However, in
general, GSGP is more robust to noise than GP in the presence of up to 10% of
noise, and presents no statistical difference for values higher than that in
the test bed.Comment: 8 pages, In proceedings of Genetic and Evolutionary Computation
Conference (GECCO 2017), Berlin, German
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