The process of treating brain cancer depends on the experience and knowledge of the physician, which may be associated with eye errors or may vary from person to person. For this reason, it is important to utilize an ...
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The process of treating brain cancer depends on the experience and knowledge of the physician, which may be associated with eye errors or may vary from person to person. For this reason, it is important to utilize an automatic tumor detection algorithm to assist radiologists and physicians for brain tumor diagnosis. The aim of the present study is to automatically detect the location of the tumor in a brain MRI image with high accuracy. For this end, in the proposed algorithm, first, the skull is separated from the brain using morphological operators. The image is then segmented by six evolutionary algorithms, i.e., Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Genetic Algorithm (GA), Differential Evolution (DE), Harmony Search (HS), and Gray Wolf Optimization (GWO), as well as two other frequently-used techniques in the literature, i.e., K-means and Otsu thresholding algorithms. Afterwards, the tumor area is isolated from the brain using the four features extracted from the main tumor. Evaluation of the segmented area revealed that the PSO has the best performance compared with the other approaches. The segmented results of the PSO are then used as the initial curve for the Active contour to precisely specify the tumor boundaries. The proposed algorithm is applied on fifty images with two different types of tumors. Experimental results on T1-weighted brain MRI images show a better performance of the proposed algorithm compared to other evolutionary algorithms, K-means, and Otsu thresholding methods.
This paper describes the novel application of an evolutionary algorithm to discriminate Parkinson's patients from age-matched controls in their response to simple figure-copying tasks. The reliable diagnosis of Pa...
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This paper describes the novel application of an evolutionary algorithm to discriminate Parkinson's patients from age-matched controls in their response to simple figure-copying tasks. The reliable diagnosis of Parkinson's disease is notoriously difficult to achieve with misdiagnosis reported to be as high as 25% of cases. The approach described in this paper aims to distinguish between the velocity profiles of pen movements of patients and controls to identify distinguishing artifacts that may be indicative of the Parkinson's symptom bradykinesia. Results are presented for 12 patients with Parkinson's disease and 10 age-match controls. An algorithm was evolved using half the patient and age-matched control responses, which was then successfully used to correctly classify the remaining responses. A more rigorous "leave one out" strategy was also applied to the test data with encouraging results.
The computation of parameters of group contribution models in order to predict thermodynamic properties usually leads to a multiparameter optimization problem where the model parameters are calculated using a regressi...
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The computation of parameters of group contribution models in order to predict thermodynamic properties usually leads to a multiparameter optimization problem where the model parameters are calculated using a regression method. A complex objective function occurs for which an optimization algorithm has to find the global minimum. Simple increment or simple group contribution models often result in unimodal regression problems, for which deterministically acting algorithms are suitable. If the model contains parameters in complex terms such as sums of exponential expressions, the optimization problem will be a nonlinear regression problem which often results in a multimodal optimization problem. In this case the search of the global or at least a fairly good optimum becomes rather difficult. evolutionary algorithms are suitable for solving such multimodal problems. Friese, T., Ulbig, P., & Schulz, S. (1998). Use of evolutionary algorithms for the calculation of group contribution parameters in order to predict thermodynamic properties (Part 1): Genetic algorithms. Computers and Chemical Engineering 22(11), 1559-1572 showed that the efficiency of genetic algorithms applied to the presented optimization problem, this paper shows that evolution strategies are suitable, as well. This work first describes the typical mode of acting of evolution strategies before a new variant, the so-called encapsulated evolution strategy using a multidimensional step-length control is introduced. This new type of strategy proved to be superior to conventional evolution strategies and genetic algorithms. In order to benefit from this new algorithm for other similar optimization problems, an optimum strategy type is determined and analyzed with the help of two visualized test systems representing the complex of optimization problems, which nonlinear parameter fittings of group contribution model parameters belong to. (C) 1999 Elsevier Science Ltd. All rights reserved.
Dynamic optimization problems involving two or more conflicting objectives appear in many real-world scenarios, and more cases are expected to appear in the near future with the increasing interest in the analysis of ...
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Dynamic optimization problems involving two or more conflicting objectives appear in many real-world scenarios, and more cases are expected to appear in the near future with the increasing interest in the analysis of streaming data sources in the context of Big Data applications. However, approaches combining dynamic multi objective optimization with preference articulation are still scarce. In this paper, we propose a new dynamic multi-objective optimization algorithm called InDM2 that allows the preferences of the decision maker (DM) to be incorporated into the search process. When solving a dynamic multi-objective optimization problem with InDM2, the DM can not only express her/his preferences by means of one or more reference points (which define the desired region of interest), but these points can be also modified interactively. InDM2 is enhanced with methods to graphically display the different approximations of the region of interest obtained during the optimization process. In this way, the DM is able to inspect and change, in optimization time, the desired region of interest according to the information displayed. We describe the main features of InDM2 and detail how It is implemented. Its performance is illustrated using both synthetic and real-world dynamic multi-objective optimization problems.
evolutionary algorithm- based size optimization of plane and space truss structures is studied for sequential loading scenarios. Size optimization of a truss is a search for the most suitable cross-sectional areas of ...
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evolutionary algorithm- based size optimization of plane and space truss structures is studied for sequential loading scenarios. Size optimization of a truss is a search for the most suitable cross-sectional areas of the truss members from the available design spaces. The novelty of the present work lies in developing an evolution- based algorithm that can consider the sequential loading scenario while designing the planner and space trusses. Fortran- based computer programming has been developed for the proposed optimization method considered here. A standard displacement- based finite element method is implemented to obtain the trusses' nodal displacement and elemental stresses. To assess the performance of the proposed algorithm three plane trusses (3- bar, 18- bar, and 200- bar) and three space trusses (22- bar, 25- bar, and 72- bar) with various displacement and stress constraints under sequential loading have been considered. The optimum weights obtained from all the problems considered here have been analyzed and compared with the same, as obtained from other methods mentioned in the existing literature. It is important to note here that the results are in close agreement and in some cases optimum weights obtained from the present study are even better than the earlier results. Finally, a real- life design problem of an industrial roof truss has been considered to assess the applicability of the proposed algorithm. The convergence study and optimum cross- section of the truss have been reported as a case study.
evolutionary algorithms (EAs) are proficient in solving the controlled, nonlinear multimodal, non-convex problems that limit the use of deterministic approaches. The competencies of EA have been applied in solving var...
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evolutionary algorithms (EAs) are proficient in solving the controlled, nonlinear multimodal, non-convex problems that limit the use of deterministic approaches. The competencies of EA have been applied in solving various environmental and water resources problems. In this study, the storm water management model (SWMM) was set up to authenticate the capability of the model for simulating catchment response in the upper Damodar River basin. Auto-calibration and validation of SWMM were done for the years 2002-2011 at a daily scale using three EAs: genetic algorithms (GAs), particle swarm optimisation (PSO) and shuffled frog leaping algorithm (SFLA). Statistical parameters like Nash-Sutcliffe effectiveness (NSE), percent bias (PBIAS) and root-mean-squared error-observations standard deviation ratio (RSR) were used to analyse the efficacy of the results. NSE and PBIAS values obtained from GA were superior, with the recorded flow with NSE and PBIAS ranging between 0.63 and 0.69 and between 1.12 and 9.81, respectively, for five discharge locations. The value of RSR was approximately 0 indicating the sensibly exceptional performance of the model. The results obtained from SFLA were robust and superior to PSO. Our results showed the prospective use and blending of the hydrodynamic model with EA would aid the decision-makers in analysing the vulnerability in river watersheds.
The design of a network is a solution to several engineering and science problems. Several network design problems are known to be NP-hard, and population-based metaheuristics like evolutionary algorithms (EAs) have b...
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The design of a network is a solution to several engineering and science problems. Several network design problems are known to be NP-hard, and population-based metaheuristics like evolutionary algorithms (EAs) have been largely investigated for such problems. Such optimization methods simultaneously generate a large number of potential solutions to investigate the search space in breadth and, consequently, to avoid local optima. Obtaining a potential solution usually involves the construction and maintenance of several spanning trees, or more generally, spanning forests. To efficiently explore the search space, special data structures have been developed to provide operations that manipulate a set of spanning trees (population). For a tree with n nodes, the most efficient data structures available in the literature require time O(n) to generate a new spanning tree that modifies an existing one and to store the new solution. We propose a new data structure, called node-depth-degree representation (NDDR), and we demonstrate that using this encoding, generating a new spanning forest requires average time O(root n). Experiments with an EA based on NDDR applied to large-scale instances of the degree-constrained minimum spanning tree problem have shown that the implementation adds small constants and lower order terms to the theoretical bound.
Convolutional neural networks (CNN) are highly effective for image classification and computer vision activities. The accuracy of CNN architecture depends on the design and selection of optimal parameters. The number ...
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Convolutional neural networks (CNN) are highly effective for image classification and computer vision activities. The accuracy of CNN architecture depends on the design and selection of optimal parameters. The number of parameters increases exponentially with every connected layer in deep CNN architecture. Therefore, the manual selection of efficient parameters entirely remains ad-hoc. To solve that problem, we must carefully examine the relationship between the depth of architecture, input parameters, and the model's accuracy. The evolutionary algorithms are prominent in solving the challenges in architecture design and parameter selection. However, the adoption of evolutionary algorithms itself is a challenging task as the computation cost increases with its evolution. The performance of evolutionary algorithms depends on the type of encoding technique used to represent a CNN architecture. In this article, we presented a comprehensive study of the recent approaches involved in the design and training of CNN architecture. The advantages and disadvantages of selecting a CNN architecture using evolutionary algorithms are discussed. The manual architecture is compared against automated CNN architecture based on the accuracy and range of parameters in the existing benchmark datasets. Furthermore, we have discussed the ongoing issues and challenges involved in evolutionary algorithms-based CNN architecture design.
Advances in miniaturization have led to the use of microchannels as heat sinks in industry. Studies have established that the thermal performance of a microchannel depends on its geometric parameters and flow conditio...
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Advances in miniaturization have led to the use of microchannels as heat sinks in industry. Studies have established that the thermal performance of a microchannel depends on its geometric parameters and flow conditions. This paper describes two approaches for determining the optimal geometric parameters of the microchannels in micro heat exchangers. One approach combines CFD analysis with an analytical method of calculating the optimal geometric parameters of micro heat exchangers. The second approach involves the usage of multi-objective genetic algorithms in combination with CFD. (c) 2005 Elsevier Ltd. All rights reserved.
Groundwater, as the key element of water resources, can play inevitably substantial role in managing groundwater aquafers. In fact, a ferocious demand for acquiring precise estimation of groundwater table is of remark...
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Groundwater, as the key element of water resources, can play inevitably substantial role in managing groundwater aquafers. In fact, a ferocious demand for acquiring precise estimation of groundwater table is of remarkable significance for analyzing water resources systems. A wide range of artificial intelligence techniques were used to predict groundwater table with highly convincing level of precision. Hence, this investigation aims to present an integration of a neuro-fuzzy (NF) system and group method of data handling (GMDH) in order to forecast the ground water table (GWT). The NF-GMDH network has been improved by means of the particle swarm optimization (PSO) and gravitational search algorithm (GSA) as evolutionary algorithms. The proposed methods were developed using records of two wells in Illinois State, USA. For this purpose, datasets related to time series of GWT have been grouped into three sections: training, testing, and validation phases. Through training and testing phases, the efficiency of the NF-GMDH methods were studied. The performances of proposed techniques were compared to the performance of radial basis function-neural network (RBF-NN). Evaluation of statistical results indicated which NF-GMDH-PSO network (R = 0.973 and RMSE = 0.545) is capable of providing higher level of precision rather than the NF-GMDH-GSA network (R = 0.969 and RMSE = 0.618) and RBF-NN (R = 0.814 and RMSE = 1.41). Also, conducting an external validation for the improved NF-GMDH models showed the most permissible level of precision.
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