scholarly journals Automated methodology for optimal selection of the minimum electrode subset for accurate EEG source localization based on Genetic Algorithm optimization

2021 ◽  
Author(s):  
Andres Soler ◽  
Luis Moctezuma ◽  
Eduardo Giraldo ◽  
Marta Molinas
Keyword(s):  
Genetic Algorithm ◽  
Source Localization ◽  
Brain Activity ◽  
Ground Truth ◽  
Multiple Source ◽  
Optimization Approach ◽  
Algorithm Optimization ◽  
Nsga Ii ◽  
Reconstruction Methods ◽  
Source Case

High-density Electroencephalography (HD-EEG) has been proven to be the most accurate option to estimate the neural activity inside the brain. Although multiple studies report the effect of electrode number on source localization for specific sources and specific electrode configurations, the electrodes for each configuration have been manually selected to uniformly cover the entire head, going from 32 to 128 electrodes, where electrodes were not selected according to their contribution to accuracy. In this work, an optimization-based study aimed to determine the minimum number of electrodes and identify optimal combinations of electrodes that can keep the localization accuracy of HD-EEG reconstructions. This optimization approach incorporates scalp landmark positions of widely used EEG montages. In this way, a systematic search for the minimum electrode subset is performed for single and multiple source localization problems. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) combined with source reconstruction methods is used to formulate a multi-objective optimization problem that minimize (1) the localization error for each source and (2) the number of required EEG electrodes. It can be used for evaluating the source localization quality of low-density EEG systems (consumer-grade wearable EEG). We performed an evaluation over synthetic and real EEG dataset with known ground-truth. The experimental results shown that selected electrode combinations with 6 electrodes can obtain for a single source case, an equal or better accuracy than HD-EEG (with more than 200 channels) when reconstructing a particular brain activity in more than 88% of the cases (in synthetic signals) and 63% (in real signals), and more than 88% and 73% of the cases when considering combinations with 8 channels. For a multiple-source case of three sources (only with synthetic signals), it was found that an equal or better accuracy than HD-EEG with 231 electrodes was attained in at least 58%, 76%, and 82% of the cases, when using optimized combinations of 8, 12, and 16 electrodes, respectively. Additionally, in such electrode numbers a lower mean error and standard deviation than with 231 electrodes was obtained.

scholarly journals Using a Goal-Switching Selection Operator in Multi-Objective Genetic Algorithm Optimization Problems

2013 ◽  
Author(s):  
Daniel Shaefer ◽  
Scott Ferguson
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Pareto Frontier ◽  
Product Line ◽  
Algorithm Optimization ◽  
Nsga Ii ◽  
Solution Quality ◽  
Design Variables ◽  
Mp3 Player ◽  
Selection Operator

This paper demonstrates how solution quality for multiobjective optimization problems can be improved by altering the selection phase of a multiobjective genetic algorithm. Rather than the traditional roulette selection used in algorithms like NSGA-II, this paper adds a goal switching technique to the selection operator. Goal switching in this context represents the rotation of the selection operator among a problem’s various objective functions to increase search diversity. This rotation can be specified over a set period of generations, evaluations, CPU time, or other factors defined by the designer. This technique is tested using a set period of generations before switching occurs, with only one objective considered at a time. Two test cases are explored, the first as identified in the Congress on Evolutionary Computation (CEC) 2009 special session and the second a case study concerning the market-driven design of a MP3 player product line. These problems were chosen because the first test case’s Pareto frontier is continuous and concave while being relatively easy to find. The second Pareto frontier is more difficult to obtain and the problem’s design space is significantly more complex. Selection operators of roulette and roulette with goal switching were tested with 3 to 7 design variables for the CEC 09 problem, and 81 design variables for the MP3 player problem. Results show that goal switching improves the number of Pareto frontier points found and can also lead to improvements in hypervolume and/or mean time to convergence.

Improved Vibration Monitoring Through Optimization of Sensor Placements

2000 ◽  
Author(s):  
Mark D. Sensmeier ◽  
Kurt L. Nichol
Keyword(s):  
Genetic Algorithm ◽  
Selection Process ◽  
Dynamic Test ◽  
Software Tool ◽  
Vibration Monitoring ◽  
Optimization Approach ◽  
Algorithm Optimization ◽  
Turbine Engine ◽  
Mode Identification ◽  
Engine Components

A PC-based software tool has been developed which optimizes the placement of sensors for vibration monitoring. This tool, called Blade-OPS, incorporates a methodology that allows the instrumentation design engineer to make tradeoffs between mode identification, mode visibility, data integrity and geometry. It uses a genetic algorithm optimization approach that simulates the natural selection process to develop an optimum design. For the blade considered here, several instrumentation configurations were selected which yield an improved fitness rating relative to the baseline sensor locations which were selected without using rigorous optimization approach. Application of this capability is not limited to turbine engine components, but will be useful for any dynamic test where instrumentation is limited.

A Non-Dominant Sorting Genetic Algorithm for Optimization of a Product Design and Selection of Its Suppliers

2020 ◽  
Vol 19 (01) ◽  
pp. 167-188
Author(s):  
Oulfa Labbi ◽  
Abdeslam Ahmadi ◽  
Latifa Ouzizi ◽  
Mohammed Douimi
Keyword(s):  
Genetic Algorithm ◽  
Product Design ◽  
Computational Time ◽  
Optimization Approach ◽  
Nsga Ii ◽  
Solution Quality ◽  
Holding Costs ◽  
Weighted Sum Method ◽  
The Impact ◽  
Selection Of

The aim of this paper is to address the problem of supplier selection in a context of an integrated product design. Indeed, the product specificities and the suppliers’ constraints are both integrated into product design phase. We consider the case of improving the design of an existing product and study the selection of its suppliers adopting a bi-objective optimization approach. Considering multi-products, multi-suppliers and multi-periods, the mathematical model proposed aims to minimize supplying, transport and holding costs of product components as well as quality rejected items. To solve the bi-objective problem, an evolutionary algorithm namely, non-dominant sorting genetic algorithm (NSGA-II) is employed. The algorithm provides a set of Pareto front solutions optimizing the two objective functions at once. Since parameters values of genetic algorithms have a significant impact on their efficiency, we have proposed to study the impact of each parameter on the fitness functions in order to determine the optimal combination of these parameters. Thus, a number of simulations evaluating the effects of crossover rate, mutation rate and number of generations on Pareto fronts are presented. To evaluate performance of the algorithm, results are compared to those obtained by the weighted sum method through a numerical experiment. According to the computational results, the non-dominant sorting genetic algorithm outperforms the CPLEX MIP solver in both solution quality and computational time.

A new method of black-box fuzzy system identification optimized by genetic algorithm and its application to predict mixture thermal properties

2019 ◽  
Vol 30 (5) ◽  
pp. 2485-2499 ◽  
Author(s):  
Wei He ◽  
Seyed Amin Bagherzadeh ◽  
Mohsen Tahmasebi ◽  
Ali Abdollahi ◽  
Mehrdad Bahrami ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Genetic Algorithm ◽  
System Identification ◽  
Fuzzy System ◽  
Black Box ◽  
Optimization Approach ◽  
Algorithm Optimization ◽  
Content Type ◽  
The Stability ◽  
Fuzzy System Identification

Purpose This paper aims to present a black-box fuzzy system identification method coupled with genetic algorithm optimization approach to predict the mixture thermal conductivity at dissimilar temperatures and nanoparticle concentrations, in the examined domains. Design/methodology/approach WO3 nanoparticles are dispersed in the deionized water to produce a homogeneous mixture at various nanoparticles mass fractions of 0.1, 0.5, 1.0 and 5.0 Wt.%. Findings The results depicted that the models not only have satisfactory precision, but also have acceptable accuracy in dealing with non-trained input values. Originality/value The transmission electron microscopy is applied to measure the mean diameters, shape and morphology of the dry nanoparticles. Moreover, the stability of nanoparticles inside the water is evaluated by using zeta potential and dynamic light scattering (DLS) tests. Then, the prepared nanofluid thermal conductivity is presented at different values of temperatures and concentrations.

scholarly journals Enhanced Genetic Algorithm Optimization Models for Vehicular Routing Problems

2019 ◽  
Vol 9 (2) ◽  
pp. 3140-3149
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Optimization Models ◽  
Optimization Approach ◽  
Algorithm Optimization ◽  
Routing Problem ◽  
Performance Factors ◽  
Fine Grained ◽  
Combinatorial Optimization Problems ◽  
Routing Strategy

The Vehicle Routing Problem (VRP) is one of the most studied combinatorial optimization problems because of its practical relevance and complexity. Though there are several techniques have been proposed to solve the VRPs and its variants effectively, each technique has its own tradeoff values in terms of the performance factors. From this perspective, the work presented in this paper proposed an intelligent routing strategy for VRP based on distance values between the cities. The proposed strategy uses an enhanced model of Genetic Algorithm to find the optimal tour paths among the cities under distance based optimized tour path estimation scenarios. For distance-based optimization approach, experiments were performed on the standard benchmark TSP instances obtained from TSPLIB. A set of fine-grained result analyses demonstrated that the proposed model of routing strategies performed comparatively better w.r.t. the existing relevant approaches. By considering this problem as the base, a distinct model was developed as a set of assistive modules for Genetic Algorithms (GA), which are aimed at improving the overall efficiency of the typical GA, particularly for optimization problems. The capability of the proposed optimization models for VRP is demonstrated at various levels, particularly at the population initialization stage, using a set of well-defined experiments.

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