How to exploit fitness landscape properties of timetabling problem: A newoperator for quantum evolutionary algorithm

© 2020 Elsevier Ltd. All rights reserved. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1016/j.eswa.2020.114211The fitness landscape of the timetabling problems is analyzed in this paper to provide some insight into theproperties of the problem. The analyses suggest that the good solutions are clustered in the search space andthere is a correlation between the fitness of a local optimum and its distance to the best solution. Inspiredby these findings, a new operator for Quantum Evolutionary Algorithms is proposed which, during the searchprocess, collects information about the fitness landscape and tried to capture the backbone structure of thelandscape. The knowledge it has collected is used to guide the search process towards a better region in thesearch space. The proposed algorithm consists of two phases. The first phase uses a tabu mechanism to collectinformation about the fitness landscape. In the second phase, the collected data are processed to guide thealgorithm towards better regions in the search space. The algorithm clusters the good solutions it has foundin its previous search process. Then when the population is converged and trapped in a local optimum, itis divided into sub-populations and each sub-population is designated to a cluster. The information in thedatabase is then used to reinitialize the q-individuals, so they represent better regions in the search space.This way the population maintains diversity and by capturing the fitness landscape structure, the algorithmis guided towards better regions in the search space. The algorithm is compared with some state-of-the-artalgorithms from PATAT competition conferences and experimental results are presented.Peer reviewe

A genetic programming-based convolutional deep learning algorithm for identifying COVID-19 cases via X-ray images

© 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Evolutionary algorithms have been successfully employed to find the best structure for many learning algorithms including neural networks. Due to their flexibility and promising results, Convolutional Neural Networks (CNNs) have found their application in many image processing applications. The structure of CNNs greatly affects the performance of these algorithms both in terms of accuracy and computational cost, thus, finding the best architecture for these networks is a crucial task before they are employed. In this paper, we develop a genetic programming approach for the optimization of CNN structure in diagnosing COVID-19 cases via X-ray images. A graph representation for CNN architecture is proposed and evolutionary operators including crossover and mutation are specifically designed for the proposed representation. The proposed architecture of CNNs is defined by two sets of parameters, one is the skeleton which determines the arrangement of the convolutional and pooling operators and their connections and one is the numerical parameters of the operators which determine the properties of these operators like filter size and kernel size. The proposed algorithm in this paper optimizes the skeleton and the numerical parameters of the CNN architectures in a co-evolutionary scheme. The proposed algorithm is used to identify covid-19 cases via X-ray images.Peer reviewe

A Novel Ensemble Machine Learning and an Evolutionary Algorithm in Modeling the COVID-19 Epidemic and Optimizing Government Policies

© 2022 IEEE. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1109/TSMC.2022.3143955The spread of the COVID-19 disease has prompted a need for immediate reaction by governments to curb the pandemic. Many countries have adopted different policies and studies are performed to understand the effect of each of the policies on the growth rate of the infected cases. In this article, the data about the policies taken by all countries at each date, and the effect of the policies on the growth rate of the pandemic are used to build a model of the pandemic's behavior. The model takes as input a set of policies and predicts the growth rate of the pandemic. Then, a population-based multi objective optimization algorithm is developed, which uses the model to search through the policy space and finds a set of policies that minimize the cost induced to the society due to the policies and the growth rate of the pandemic. Because of the complexity of the modeling problem and the uncertainty in measuring the growth rate of the pandemic via the models, an ensemble learning algorithm is proposed in this article to improve the performance of individual learning algorithms. The ensemble consists of ten learning algorithms and a metamodel algorithm that is built to predict the accuracy of each learning algorithm for a given data record. The metamodel is a set of support vector machine (SVM) algorithms that is used in the aggregation phase of the ensemble algorithm. Because there is uncertainty in measuring the growth rate via the models, a landscape smoothing operator is proposed in the optimization process, which aims at reducing uncertainty. The algorithm is tested on open access data online and experiments on the ensemble learning and the policy optimization algorithms are performed.Peer reviewe

An evolutionary ensemble learning for diagnosing COVID-19 via cough signals

© 2023 Published by Elsevier B.V. on behalf of Chinese Medical Association. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)Objective The spread of the COVID-19 disease has caused great concern around the world and detecting the positive cases is crucial in curbing the pandemic. One of the symptoms of the disease is the dry cough it causes. It has previously been shown that cough signals can be used to identify a variety of diseases including tuberculosis, asthma, etc. In this paper, we proposed an algorithm to diagnose the COVID-19 disease via cough signals. Methods The proposed algorithm was an ensemble scheme that consists of a number of base learners, where each base learner used a different feature extractor method, including statistical approaches and convolutional neural networks (CNNs) for automatic feature extraction. Features were extracted from the raw signal and some transforms performed it, including Fourier, wavelet, Hilbert-Huang, and short-term Fourier transforms. The outputs of these base-learners were aggregated via a weighted voting scheme, with the weights optimised via an evolutionary paradigm. This paper also proposed a memetic algorithm for training the CNNs in the base-learners, which combined the speed of gradient descent (GD) algorithms and global search space coverage of the evolutionary algorithms. Results Experiments were performed on the proposed algorithm and different rival algorithms which included a number of CNN architectures in the literature and generic machine learning algorithms. The results suggested that the proposed algorithm achieves better performance compared to the existing algorithms in diagnosing COVID-19 via cough signals. Conclusion COVID-19 may be diagnosed via cough signals and CNNs may be employed to process these signals and it may be further improved by the optimization of CNN architecture.Peer reviewe

Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

Applications of Artificial Intelligence in Battling Against Covid-19: A Literature Review

© 2020 Elsevier Ltd. All rights reserved.Colloquially known as coronavirus, the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19), has become a matter of grave concern for every country around the world. The rapid growth of the pandemic has wreaked havoc and prompted the need for immediate reactions to curb the effects. To manage the problems, many research in a variety of area of science have started studying the issue. Artificial Intelligence is among the area of science that has found great applications in tackling the problem in many aspects. Here, we perform an overview on the applications of AI in a variety of fields including diagnosis of the disease via different types of tests and symptoms, monitoring patients, identifying severity of a patient, processing covid-19 related imaging tests, epidemiology, pharmaceutical studies, etc. The aim of this paper is to perform a comprehensive survey on the applications of AI in battling against the difficulties the outbreak has caused. Thus we cover every way that AI approaches have been employed and to cover all the research until the writing of this paper. We try organize the works in a way that overall picture is comprehensible. Such a picture, although full of details, is very helpful in understand where AI sits in current pandemonium. We also tried to conclude the paper with ideas on how the problems can be tackled in a better way and provide some suggestions for future works.Peer reviewe

Gas diffusion layer characterization and microstructural modeling in polymer electrolyte fuel cells

Polymer electrolyte fuel cells (PEFCs), as promising clean energy power sources, are potential substitutes not only for stationary power generation but also for mobile applications specifically in transportation due to their high power density and performance as well as lack of pollutants. PEFC vehicles are at the dawn of commercialization, but still, cost, performance, and durability of current PEFCs need to be further improved to facilitate vast market integration especially under high current density conditions. Pursuant to this goal, comprehensive multidisciplinary understanding of multiphase transport of mass, heat, and electricity in the PEFC constituents including the gas diffusion layer (GDL), as the centerpiece of this thesis, will help to make progress towards material optimization and subsequently fuel cell performance improvements. The GDL transport capability is determined by its effective transport properties which are strongly dependent on its morphological, microstructural, and physical characteristics. Therefore, accurate knowledge regarding the correlation between the GDL microstructure and its transport properties is essential for improving the performance and durability of PEFCs as well as for material optimization, fuel cell design, and prototyping in the area of fuel cell development and manufacturing. In this context, this thesis aims to develop a fast and cost-effective design tool for GDL microstructural modeling and transport properties simulation. Given the limitations of experimental, analytical, and tomographic techniques, stochastic microstructural model development to retrieve the heterogeneous GDL microstructure is a more reliable and flexible tool for GDL material design and prototyping assignments to reduce cost and time of the design cycle. Inspired by the randomness of the GDL porous media structure and its fabrication process, the GDL microstructure is virtually reconstructed as a collection of stochastic processes to provide a robust representation of the structure. The technique of stochastic microstructural reconstruction relies on statistical correlation functions which describe the probabilities of the porous media constituents’ distribution and aim to encompass all the details of the porous media. The obtained 3D digitized realizations of the stochastic model are then used as a domain for numerical computation of transport properties. In this thesis, a unique stochastic GDL microstructural modeling framework inspired by manufacturing information and characterization data is developed in which all GDL substrate and MPL components are resolved, and thoroughly validated with literature and measured data for a variety of MPL-coated GDLs. The effects of PTFE loading and liquid water saturation on the GDL substrate anisotropic transport properties for both gas and liquid phases are found to be highly coupled and are therefore simulated and analyzed jointly. Furthermore, a parametric study is conducted to investigate the effect of MPL pore morphology composition on the MPL and MPL-coated GDL transport properties. The validated stochastic design tool can be used as a fast and accurate framework for reconstructing GDL porous materials and understanding the correlation between the GDL morphology and transport properties. This paves the way for development of improved GDL materials with desired transport properties in modern PEFCs

Cancer and Its Treatment in Main Ancient Books of Islamic Iranian Traditional Medicine (7th to 14th Century AD)

ABSTRACT: Islamic medicine is regarded as a comprehensive medical school with a long, glorious and worldwide reputation. Some of the physicians of this school are famous worldwide and have contributed valuable services to the scientific world. Given the dramatically increasing prevalence of cancer and the relative inefficacy of current medications, there is a great demand for the introduction of effective therapeutic approaches. To this end, integration of traditional medicine with modern medical treatments represents a promising option. In this essay, methods of diagnosis and treatment of cancer have been mentioned from the viewpoint of five famous physicians before the Mongolian attack who used Islamic medicine, namely Rhazes, Akhaveyni, Ahwazi, Avicenna and Jorjani. The ideas discussed dates back to a period between the eighth and fourteenth centuries