Search results for "Genetic algorithm"

Hybrid Particle Swarm Optimization With Genetic Algorithm to Train Artificial Neural Networks for Short-Term Load Forecasting

2019

This research proposes a new training algorithm for artificial neural networks (ANNs) to improve the short-term load forecasting (STLF) performance. The proposed algorithm overcomes the so-called training issue in ANNs, where it traps in local minima, by applying genetic algorithm operations in particle swarm optimization when it converges to local minima. The training ability of the hybridized training algorithm is evaluated using load data gathered by Electricity Generating Authority of Thailand. The ANN is trained using the new training algorithm with one-year data to forecast equal 48 periods of each day in 2013. During the testing phase, a mean absolute percentage error (MAPE) is used …

Tabu and Scatter Search for Artificial Neural Networks

2003

In this paper we address the problem of training multilayer feed-forward neural networks. These networks have been widely used for both prediction and classification in many different areas. Although the most popular method for training these networks is back propagation, other optimization methods such as tabu search or scatter search have been applied to solve this problem. This paper presents a new training algorithm based on the tabu search methodology that incorporates elements for search intensification and diversification by utilizing strategic designs where other previous approaches resort to randomization. Our method considers context and search information, as it is provided by th…

A 4K-Input High-Speed Winner-Take-All (WTA) Circuit with Single-Winner Selection for Change-Driven Vision Sensors

2019

Winner-Take-All (WTA) circuits play an important role in applications where a single element must be selected according to its relevance. They have been successfully applied in neural networks and vision sensors. These applications usually require a large number of inputs for the WTA circuit, especially for vision applications where thousands to millions of pixels may compete to be selected. WTA circuits usually exhibit poor response-time scaling with the number of competitors, and most of the current WTA implementations are designed to work with less than 100 inputs. Another problem related to the large number of inputs is the difficulty to select just one winner, since many competitors ma…

A new method for optimal synthesis of wavelet-based neural networks suitable for identification purposes

1999

Abstract This paper deals with a new method for optimal synthesis of Wavelet-Based Neural Networks (WBNN) suitable for identification purposes. The method uses a genetic algorithm (GA) combined with a steepest descent technique and least square techniques for both optimal selection of the structure of the WBNN and its training. The method is applied for designing a predictor for a chaotic temporal series

Applicant reactions and faking in real-life personnel selection

2011

Honkaniemi, L., Tolvanen, A. & Feldt, T. (2011). Applicant reactions and faking in real-life personnel selection. Scandinavian Journal of Psychology 52, 376–381. Faking may affect hiring decisions in personnel selection. All the antecedents of faking are still not known. The present study investigates the association between applicants’ reactions about the selection procedure and their tendency to fake. The subjects (N = 180) were real-life applicants for a fire and rescue personnel school. After completing the selection process, the applicants filled out a questionnaire about their test reactions (Chan, Schmitt, Sacco & DeSohon, 1998b) and a faking scale, the Balanced Inventory of Desirabl…

In silico and in vitro comparative analysis to select, validate and test SNPs for human identification.

2007

Abstract Background The recent advances in human genetics have recently provided new insights into phenotypic variation and genome variability. Current forensic DNA techniques involve the search for genetic similarities and differences between biological samples. Consequently the selection of ideal genomic biomarkers for human identification is crucial in order to ensure the highest stability and reproducibility of results. Results In the present study, we selected and validated 24 SNPs which are useful in human identification in 1,040 unrelated samples originating from three different populations (Italian, Benin Gulf and Mongolian). A Rigorous in silico selection of these markers provided …

Human factor policy testing in the sequencing of manual mixed model assembly lines

2004

In this paper the human resource management in manual mixed model assembly U-lines is considered. The objective is to minimise the total conveyor stoppage time to achieve the full efficiency of the line. A model, that includes effects of the human resource, was developed in order to evaluate human factor policies impact on the optimal solution of this line sequencing problem. Different human resource management policies are introduced to cope with the particular layout of the proposed line. Several examples have been proposed to investigate the effects of line dimensions on the proposed management policies. The examples have been solved through a genetic algorithm. The obtained results conf…

Phenotype Correlation and Intergenerational Dynamics of the Friedreich Ataxia GAA Trinucleotide Repeat

1997

Summary The Friedreich ataxia (FA) mutation has recently been identified as an unstable trinucleotide GAA repeat present 7–22 times in the normal population but amplified as many as > 1, 000 times in FA. Since it is an autosomal recessive disease, FA does not show typical features observed in other dynamic mutation disorders, such as genetic anticipation. We have analyzed the GAA repeat in 104 FA patients and 163 carrier relatives previously defined by linkage analysis. The GAA expansion was detected in all patients, most (94%) of them being ho-mozygous for the mutation. We have demonstrated that clinical variability in FA is related to the size of the expanded alleles: milder forms of the …

2017

Abstract. We present a Monte Carlo genetic algorithm (MCGA) for efficient, automated, and unbiased global optimization of model input parameters by simultaneous fitting to multiple experimental data sets. The algorithm was developed to address the inverse modelling problems associated with fitting large sets of model input parameters encountered in state-of-the-art kinetic models for heterogeneous and multiphase atmospheric chemistry. The MCGA approach utilizes a sequence of optimization methods to find and characterize the solution of an optimization problem. It addresses an issue inherent to complex models whose extensive input parameter sets may not be uniquely determined from limited in…

Automation of Optimized Gabor Filter Parameter Selection for Road Cracks Detection

2016

International audience; Automated systems for road crack detection are extremely important in road maintenance for vehicle safety and traveler's comfort. Emerging cracks in roads need to be detected and accordingly repaired as early as possible to avoid further damage thus reducing rehabilitation cost. In this paper, a robust method for Gabor filter parameters optimization for automatic road crack detection is discussed. Gabor filter has been used in previous literature for similar applications. However, there is a need for automatic selection of optimized Gabor filter parameters due to variation in texture of roads and cracks. The problem of change of background, which in fact is road text…