scholarly journals CIXL2: A Crossover Operator for Evolutionary Algorithms Based on Population Features

2005 ◽  
Vol 24 ◽  
pp. 1-48 ◽  
Author(s):  
D. Ortiz-Boyer ◽  
C. Hervás-Martínez ◽  
N. García-Pedrajas
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Evolutionary Algorithms ◽  
Statistical Tests ◽  
Search Process ◽  
Crossover Operator ◽  
Exploration And Exploitation ◽  
Standard Methods ◽  
Population Distributions ◽  
Proposed Model

In this paper we propose a crossover operator for evolutionary algorithms with real values that is based on the statistical theory of population distributions. The operator is based on the theoretical distribution of the values of the genes of the best individuals in the population. The proposed operator takes into account the localization and dispersion features of the best individuals of the population with the objective that these features would be inherited by the offspring. Our aim is the optimization of the balance between exploration and exploitation in the search process. In order to test the efficiency and robustness of this crossover, we have used a set of functions to be optimized with regard to different criteria, such as, multimodality, separability, regularity and epistasis. With this set of functions we can extract conclusions in function of the problem at hand. We analyze the results using ANOVA and multiple comparison statistical tests. As an example of how our crossover can be used to solve artificial intelligence problems, we have applied the proposed model to the problem of obtaining the weight of each network in a ensemble of neural networks. The results obtained are above the performance of standard methods.

scholarly journals Studies of Manufacturing Process Control: A Technological Transfer of Soft Computing

2008 ◽  
pp. 31-37
Author(s):  
J. P. Panda ◽  
R. N. Satpathy
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Artificial Neural Networks ◽  
Technology Transfer ◽  
Evolutionary Algorithms ◽  
Soft Computing ◽  
Manufacturing Processes ◽  
Technological Transfer ◽  
Manufacturing Process Control

The field of soft computing embraces several techniques that have been inspired by nature but are mathematical. These techniques are artificial neural networks, fuzzy logic and evolutionary algorithms. Often these techniques are considered part of artificial intelligence, however the name artificial intelligence is more properly given to techniques which try to capture and emulate biological intelligence, such as expert systems and thinking computers. This paper focuses on the technology transfer issues and solutions when using soft computing for off line control of manufacturing processes. This paper will discuss each of these three techniques – neural networks, fuzzy logic and evolutionary algorithms - in turn and how they might be used in manufacturing. The kind of problems these techniques are best suited for will be defined, and competing techniques will be compared and contrasted.

scholarly journals QUANTUM-INSPIRED ARTIFICIL NEURAL NETWORKS AND EVOLUTIONARY ALGORITHMS METHODS APPLIED TO MODELING OF THE POLISH ELECTRIC POWER EXCHANGE USING THE DAY-AHEAD MARKET DATA

2018 ◽  
Vol 7 (3) ◽  
pp. 201-212
Author(s):  
Jerzy Tchórzewski ◽  
Dariusz Ruciński
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Quantum Computing ◽  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Computer Science ◽  
Electric Power ◽  
Quantum Computer ◽  
Neural Model ◽  
Power Exchange

The paper presents selected results of research on the use of artificial intelligence methods, which are inspired by quantum computing solutions for modelling of electric power exchange systems. Methods used in the modelling of quantum data acquisition, quantization and dequantization of information as well as the methods of performing quantum computations were emphasized. Furthermore, we have analysed the results obtained for the neural model and for the evolutionary algorithm inspired by the quantum computer science. Eventually, the model was verified on the example of the neural model of the Electric Power Exchange (EPE).

scholarly journals An Efficient Model for Financial Risks Assessment Based on Artificial Neural Networks

2020 ◽  
Vol 55 (3) ◽  
Author(s):  
Hayder M. Kareem Al_Duhaidahawi ◽  
Jing Z S. Abdulreza ◽  
Meriem Sebai ◽  
Sinan Abdullah Harjan
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Risk Assessment ◽  
Risk Management ◽  
Artificial Neural Networks ◽  
Financial Risk ◽  
Banking Sector ◽  
Financial Risks ◽  
Proposed Model ◽  
Artificial Neural

According to the developments in financial liberalization and banking innovation, the bank risks have been changed in their nature which leads to use new financial instruments. Thus banks increasingly adopt risk assessment to avoid it. Therefore, this article describes a new model to assist financial risk management based on artificial intelligence. This entails using artificial neural networks to forecast financial risks and support the decision-makers and the consumers in making better risk management decisions. A real-world case study based on the Iraqi banking sector is presented to guarantee the applicability, accuracy, and efficiency of our proposed model. The sample was selected from a data of 16 banks for the period (2004-2018), taken from Iraq Securities Commission, regular market (https://www.isc.gov.iq/). The data were examined with an initial analysis and then converted to the formula compatible with neural networks. The authors describe the results obtained and compare them with previous studies. It confirmed the effectiveness of the proposed model for risk assessment by the results obtained from the approved form on artificial intelligence.

scholarly journals AI Methods in Algorithmic Composition: A Comprehensive Survey

2013 ◽  
Vol 48 ◽  
pp. 513-582 ◽  
Author(s):  
J.D. Fernandez ◽  
F. Vico
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Evolutionary Algorithms ◽  
Constraint Programming ◽  
Music Composition ◽  
Probabilistic Methods ◽  
Computational Techniques ◽  
Algorithmic Composition ◽  
Rule Based ◽  
Comprehensive Survey

Algorithmic composition is the partial or total automation of the process of music composition by using computers. Since the 1950s, different computational techniques related to Artificial Intelligence have been used for algorithmic composition, including grammatical representations, probabilistic methods, neural networks, symbolic rule-based systems, constraint programming and evolutionary algorithms. This survey aims to be a comprehensive account of research on algorithmic composition, presenting a thorough view of the field for researchers in Artificial Intelligence.

Using neural networks and artificial intelligence as a modern method of glaucoma diagnosis

2020 ◽  
pp. 27-28
Author(s):  
A.B. Movsisyan ◽  
◽  
A.V. Kuroyedov ◽  
G.A. Ostapenko ◽  
S.V. Podvigin ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Modern Method ◽  
Glaucoma Diagnosis

Актуальность. Определяется увеличением заболеваемости глаукомой во всем мире как одной из основных причин снижения зрения и поздней постановкой диагноза при имеющихся выраженных изменений со стороны органа зрения. Цель. Повысить эффективность диагностики глаукомы на основании оценки диска зрительного нерва и перипапиллярной сетчатки нейросетью и искусственным интеллектом. Материал и методы. Для обучения нейронной сети были выделены четыре диагноза: первый – «норма», второй – начальная глаукома, третий – развитая стадия глаукомы, четвертый – глаукома далеко зашедшей стадии. Классификация производилась на основе снимков глазного дна: область диска зрительного нерва и перипапиллярной сетчатки. В результате классификации входные данные разбивались на два класса «норма» и «глаукома». Для целей обучения и оценки качества обучения, множество данных было разбито на два подмножества: тренировочное и тестовое. В тренировочное подмножество были включены 8193 снимка с глаукомными изменениями диска зрительного нерва и «норма» (пациенты без глаукомы). Стадии заболевания были верифицированы согласно действующей классификации первичной открытоугольной глаукомы 3 (тремя) экспертами со стажем работы от 5 до 25 лет. В тестовое подмножество были включены 407 снимков, из них 199 – «норма», 208 – с начальной, развитой и далекозашедшей стадиями глаукомы. Для решения задачи классификации на «норма»/«глаукома» была выбрана архитектура нейронной сети, состоящая из пяти сверточных слоев. Результаты. Чувствительность тестирования дисков зрительных нервов с помощью нейронной сети составила 0,91, специфичность – 0,93. Анализ полученных результатов работы показал эффективность разработанной нейронной сети и ее преимущество перед имеющимися методами диагностики глаукомы. Выводы. Использование нейросетей и искусственного интеллекта является современным, эффективным и перспективным методом диагностики глаукомы.

scholarly journals Neural Network Approach for Global Solar Irradiance Prediction at Extremely Short-Time-Intervals Using Particle Swarm Optimization Algorithm

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1213
Author(s):  
Ahmed Aljanad ◽  
Nadia M. L. Tan ◽  
Vassilios G. Agelidis ◽  
Hussain Shareef
Keyword(s):  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Solar Irradiance ◽  
Particle Swarm ◽  
Time Interval ◽  
Time Intervals ◽  
Swarm Optimization ◽  
Backpropagation Neural Networks ◽  
Proposed Model ◽  
Short Time

Hourly global solar irradiance (GSR) data are required for sizing, planning, and modeling of solar photovoltaic farms. However, operating and controlling such farms exposed to varying environmental conditions, such as fast passing clouds, necessitates GSR data to be available for very short time intervals. Classical backpropagation neural networks do not perform satisfactorily when predicting parameters within short intervals. This paper proposes a hybrid backpropagation neural networks based on particle swarm optimization. The particle swarm algorithm is used as an optimization algorithm within the backpropagation neural networks to optimize the number of hidden layers and neurons used and its learning rate. The proposed model can be used as a reliable model in predicting changes in the solar irradiance during short time interval in tropical regions such as Malaysia and other regions. Actual global solar irradiance data of 5-s and 1-min intervals, recorded by weather stations, are applied to train and test the proposed algorithm. Moreover, to ensure the adaptability and robustness of the proposed technique, two different cases are evaluated using 1-day and 3-days profiles, for two different time intervals of 1-min and 5-s each. A set of statistical error indices have been introduced to evaluate the performance of the proposed algorithm. From the results obtained, the 3-days profile’s performance evaluation of the BPNN-PSO are 1.7078 of RMSE, 0.7537 of MAE, 0.0292 of MSE, and 31.4348 of MAPE (%), at 5-s time interval, where the obtained results of 1-min interval are 0.6566 of RMSE, 0.2754 of MAE, 0.0043 of MSE, and 1.4732 of MAPE (%). The results revealed that proposed model outperformed the standalone backpropagation neural networks method in predicting global solar irradiance values for extremely short-time intervals. In addition to that, the proposed model exhibited high level of predictability compared to other existing models.

scholarly journals Training Artificial Intelligence Neural Networks to Detect Devices on a Chest X-ray, a Pathway to COVID-19 Testing

2020 ◽  
Vol 112 (5) ◽  
pp. S50
Author(s):  
Zachary Eller ◽  
Michelle Chen ◽  
Jermaine Heath ◽  
Uzma Hussain ◽  
Thomas Obisean ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
X Ray ◽  
Chest X Ray
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