Feature Reduction Using Genetic Algorithm for Cognitive Man-Machine Communication

2015 ◽  
Vol 7 (4) ◽  
pp. 1-17
Author(s):  
Naveen Irtiza ◽  
Humera Farooq
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Feature Space ◽  
Feature Reduction ◽  
Machine Learning Techniques ◽  
Brain State ◽  
Cognitive State ◽  
Eeg Signals ◽  
Eeg Recordings ◽  
Machine Communication

Electroencephalographic (EEG) signals are usually comprised of high-dimensional feature space. This work aims to assess the effect of reducing the number of features extracted from EEG recordings. A methodology is proposed that combines brain imaging and machine learning techniques to predict the cognitive state of the subjects whether they are feeling themselves in a safe or dangerous environment. The changes in the brain state are correlated with power modulations of oscillatory rhythms in recorded EEG signals called ERD / ERS (Event-related De-synchronization / Synchronization). In order to determine the optimized number of features, Genetic Algorithm (GA) will be used. GA has played instrumental role in solving optimization problems from diverse fields. In various studies and researches for Cognitive Man-Machine Communication, the algorithm has been used as an effective method to extract an optimal set of features.

Forking Genetic Algorithms: GAs with Search Space Division Schemes

1997 ◽  
Vol 5 (1) ◽  
pp. 61-80 ◽  
Author(s):  
Shigeyoshi Tsutsui ◽  
Yoshiji Fujimoto ◽  
Ashish Ghosh
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Complex Function ◽  
Feature Space ◽  
Search Space ◽  
Function Optimization ◽  
The Other ◽  
Parent Population ◽  
New Type ◽  
Multimodal Problems

In this article, we propose a new type of genetic algorithm (GA), the forking GA (fGA), which divides the whole search space into subspaces, depending on the convergence status of the population and the solutions obtained so far. The fGA is intended to deal with multimodal problems that are difficult to solve using conventional GAs. We use a multi-population scheme that includes one parent population that explores one subspace and one or more child populations exploiting the other subspace. We consider two types of fGAs, depending on the method used to divide the search space. One is the genoqtypic fGA (g-fGA), which defines the search subspace for each subpopulation, depending on the salient schema within the genotypic search space. The other is the phenotypic fGA (p-fGA), which defines a search subspace by a neighborhood hypercube around the current best individual in the phenotypic feature space. Empirical results on complex function optimization problems show that both the g-fGA and the p-GA perform well compared to conventional GAs. Two additional utilities of the p-fGA are also studied briefly.

scholarly journals Determinant of Covariance Matrix Model Coupled with AdaBoost Classification Algorithm for EEG Seizure Detection

Diagnostics ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 74
Author(s):  
Shahab Abdulla ◽  
Mohammed Diykh ◽  
Sarmad K. D. Alkhafaji ◽  
Jonathan H. Greena ◽  
Hanan Al-Hadeethi ◽  
...  
Keyword(s):  
Covariance Matrix ◽  
Back Propagation ◽  
Epileptic Seizures ◽  
Back Propagation Neural Network ◽  
Machine Learning Techniques ◽  
Eeg Signals ◽  
Average Accuracy ◽  
Effective Selection ◽  
Kolmogorov Smirnov ◽  
Eeg Recordings

Experts usually inspect electroencephalogram (EEG) recordings page-by-page in order to identify epileptic seizures, which leads to heavy workloads and is time consuming. However, the efficient extraction and effective selection of informative EEG features is crucial in assisting clinicians to diagnose epilepsy accurately. In this paper, a determinant of covariance matrix (Cov–Det) model is suggested for reducing EEG dimensionality. First, EEG signals are segmented into intervals using a sliding window technique. Then, Cov–Det is applied to each interval. To construct a features vector, a set of statistical features are extracted from each interval. To eliminate redundant features, the Kolmogorov–Smirnov (KST) and Mann–Whitney U (MWUT) tests are integrated, the extracted features ranked based on KST and MWUT metrics, and arithmetic operators are adopted to construe the most pertinent classified features for each pair in the EEG signal group. The selected features are then fed into the proposed AdaBoost Back-Propagation neural network (AB_BP_NN) to effectively classify EEG signals into seizure and free seizure segments. Finally, the AB_BP_NN is compared with several classical machine learning techniques; the results demonstrate that the proposed mode of AB_BP_NN provides insignificant false positive rates, simpler design, and robustness in classifying epileptic signals. Two datasets, the Bern–Barcelona and Bonn datasets, are used for performance evaluation. The proposed technique achieved an average accuracy of 100% and 98.86%, respectively, for the Bern–Barcelona and Bonn datasets, which is considered a noteworthy improvement compared to the current state-of-the-art methods.

A NOVEL HYBRID FRAMEWORK OF COEVOLUTIONARY GA AND MACHINE LEARNING

2002 ◽  
Vol 02 (01) ◽  
pp. 33-52 ◽  
Author(s):  
HISASHI HANDA ◽  
MITSURU BABA ◽  
TADASHI HORIUCHI ◽  
OSAMU KATAI
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Optimization Problems ◽  
Constraint Satisfaction Problems ◽  
Function Optimization ◽  
Machine Learning Techniques ◽  
Multidimensional Knapsack ◽  
Learning Techniques ◽  
Hybrid Framework ◽  
Coevolutionary Genetic Algorithm

In this paper, we will propose a novel framework of hybridization of Coevolutionary Genetic Algorithm and Machine Learning. The Coevolutionary Genetic Algorithm (CGA) which has already been proposed by Handa et al. consists of two GA populations: the first GA (H-GA) population searches for the solutions in given problems, and the second GA (P-GA) population searches for effective schemata of the H-GA. The CGA adopts the notion of commensalism, a kind of co-evolution. The new hybrid framework incorporates a schema extraction mechanism by Machine Learning techniques into the CGA. Considerable improvement in its search ability is obtained by extracting more efficient and useful schemata from the H-GA population and then by incorporating those extracted schemata into the P-GA. We will examine and compare two kinds of machine learning techniques in extracting schema information: C4.5 and CN2. Several computational simulations on multidimensional knapsack problems, constraint satisfaction problems and function optimization problems will reveal the effectiveness of the proposed methods.

scholarly journals Hyperscanning EEG and Classification Based on Riemannian Geometry for Festive and Violent Mental State Discrimination

2020 ◽  
Vol 14 ◽  
Author(s):  
Cédric Simar ◽  
Ana-Maria Cebolla ◽  
Gaëlle Chartier ◽  
Mathieu Petieau ◽  
Gianluca Bontempi ◽  
...  
Keyword(s):  
Riemannian Geometry ◽  
Mental States ◽  
Machine Learning Techniques ◽  
Eeg Signals ◽  
Action Execution ◽  
Distinctive Features ◽  
Non Invasive ◽  
State Discrimination ◽  
Learning Techniques ◽  
Eeg Recordings

Interactions between two brains constitute the essence of social communication. Daily movements are commonly executed during social interactions and are determined by different mental states that may express different positive or negative behavioral intent. In this context, the effective recognition of festive or violent intent before the action execution remains crucial for survival. Here, we hypothesize that the EEG signals contain the distinctive features characterizing movement intent already expressed before movement execution and that such distinctive information can be identified by state-of-the-art classification algorithms based on Riemannian geometry. We demonstrated for the first time that a classifier based on covariance matrices and Riemannian geometry can effectively discriminate between neutral, festive, and violent mental states only on the basis of non-invasive EEG signals in both the actor and observer participants. These results pave the way for new electrophysiological discrimination of mental states based on non-invasive EEG recordings and cutting-edge machine learning techniques.

scholarly journals Emotional Stress State Detection Using Genetic Algorithm-Based Feature Selection on EEG Signals

2018 ◽  
Vol 15 (11) ◽  
pp. 2461 ◽  
Author(s):  
Dongkoo Shon ◽  
Kichang Im ◽  
Jeong-Ho Park ◽  
Dong-Sun Lim ◽  
Byungtae Jang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Stress State ◽  
Stress Analysis ◽  
Classification Accuracy ◽  
Machine Learning Techniques ◽  
Human Beings ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Alpha Asymmetry

In recent years, stress analysis by using electro-encephalography (EEG) signals incorporating machine learning techniques has emerged as an important area of research. EEG signals are one of the most important means of indirectly measuring the state of the brain. The existing stress algorithms lack efficient feature selection techniques to improve the performance of a subsequent classifier. In this paper, genetic algorithm (GA)-based feature selection and k-nearest neighbor (k-NN) classifier are used to identify stress in human beings by analyzing electro-encephalography (EEG) signals. GA is incorporated in the stress analysis pipeline to effectively select subset of features that are suitable to enhance the performance of the k-NN classifier. The performance of the proposed method is evaluated using the Database for Emotion Analysis using Physiological Signals (DEAP), which is a public EEG dataset. A feature set is extracted in 32 EEG channels, which consists of statistical features, Hjorth parameters, band power, and frontal alpha asymmetry. The selected features through GA are used as input to the k-NN classifier to distinguish whether each EEG datapoint represents a stress state. To further consolidate, the effectiveness of the proposed method is compared with that of a state-of-the-art principle component analysis (PCA) method. Experimental results show that the proposed GA-based method outperforms PCA, with GA demonstrating 71.76% classification accuracy compared with 65.3% for PCA. Thus, it can be concluded that the proposed method can be effectively used for stress analysis with high classification accuracy.

Binary Spectrum Feature for Improved Classifier Performance

2020 ◽  
Author(s):  
Nalika Ulapane ◽  
Karthick Thiyagarajan ◽  
sarath kodagoda
Keyword(s):  
Machine Learning ◽  
Classification Performance ◽  
Feature Reduction ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Monitoring Task ◽  
Classifier Performance ◽  
Spectrum Feature

<div>Classification has become a vital task in modern machine learning and Artificial Intelligence applications, including smart sensing. Numerous machine learning techniques are available to perform classification. Similarly, numerous practices, such as feature selection (i.e., selection of a subset of descriptor variables that optimally describe the output), are available to improve classifier performance. In this paper, we consider the case of a given supervised learning classification task that has to be performed making use of continuous-valued features. It is assumed that an optimal subset of features has already been selected. Therefore, no further feature reduction, or feature addition, is to be carried out. Then, we attempt to improve the classification performance by passing the given feature set through a transformation that produces a new feature set which we have named the “Binary Spectrum”. Via a case study example done on some Pulsed Eddy Current sensor data captured from an infrastructure monitoring task, we demonstrate how the classification accuracy of a Support Vector Machine (SVM) classifier increases through the use of this Binary Spectrum feature, indicating the feature transformation’s potential for broader usage.</div><div><br></div>

Enterprise distribution routing optimization with soft time windows based on genetic algorithm

2021 ◽  
pp. 1-11
Author(s):  
Kaixian Gao ◽  
Guohua Yang ◽  
Xiaobo Sun
Keyword(s):  
Genetic Algorithm ◽  
Customer Satisfaction ◽  
Optimization Problems ◽  
Rapid Development ◽  
Time Windows ◽  
Theory And Practice ◽  
Routing Problem ◽  
Logistics Industry ◽  
Routing Optimization ◽  
A Company

With the rapid development of the logistics industry, the demand of customer become higher and higher. The timeliness of distribution becomes one of the important factors that directly affect the profit and customer satisfaction of the enterprise. If the distribution route is planned rationally, the cost can be greatly reduced and the customer satisfaction can be improved. Aiming at the routing problem of A company’s vehicle distribution link, we establish mathematical models based on theory and practice. According to the characteristics of the model, genetic algorithm is selected as the algorithm of path optimization. At the same time, we simulate the actual situation of a company, and use genetic algorithm to plan the calculus. By contrast, the genetic algorithm suitable for solving complex optimization problems, the practicability of genetic algorithm in this design is highlighted. It solves the problem of unreasonable transportation of A company, so as to get faster efficiency and lower cost.

scholarly journals bCNN-Methylpred: Feature-Based Prediction of RNA Sequence Modification Using Branch Convolutional Neural Network

Genes ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1155
Author(s):  
Naeem Islam ◽  
Jaebyung Park
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Domain Knowledge ◽  
Feature Space ◽  
Experimental Methods ◽  
Machine Learning Techniques ◽  
Rna Modification ◽  
Biological Processes ◽  
Rna Sequence ◽  
Functional Mechanisms

RNA modification is vital to various cellular and biological processes. Among the existing RNA modifications, N6-methyladenosine (m6A) is considered the most important modification owing to its involvement in many biological processes. The prediction of m6A sites is crucial because it can provide a better understanding of their functional mechanisms. In this regard, although experimental methods are useful, they are time consuming. Previously, researchers have attempted to predict m6A sites using computational methods to overcome the limitations of experimental methods. Some of these approaches are based on classical machine-learning techniques that rely on handcrafted features and require domain knowledge, whereas other methods are based on deep learning. However, both methods lack robustness and yield low accuracy. Hence, we develop a branch-based convolutional neural network and a novel RNA sequence representation. The proposed network automatically extracts features from each branch of the designated inputs. Subsequently, these features are concatenated in the feature space to predict the m6A sites. Finally, we conduct experiments using four different species. The proposed approach outperforms existing state-of-the-art methods, achieving accuracies of 94.91%, 94.28%, 88.46%, and 94.8% for the H. sapiens, M. musculus, S. cerevisiae, and A. thaliana datasets, respectively.

scholarly journals Optimization of a 3D-Printed Permanent Magnet Coupling Using Genetic Algorithm and Taguchi Method

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 494
Author(s):  
Ekaterina Andriushchenko ◽  
Ants Kallaste ◽  
Anouar Belahcen ◽  
Toomas Vaimann ◽  
Anton Rassõlkin ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Taguchi Method ◽  
Permanent Magnet ◽  
Optimization Problems ◽  
Optimization Method ◽  
Taguchi Optimization ◽  
Torque Density ◽  
Electromagnetic Devices ◽  
3D Printed ◽  
Manufacturing Techniques

In recent decades, the genetic algorithm (GA) has been extensively used in the design optimization of electromagnetic devices. Despite the great merits possessed by the GA, its processing procedure is highly time-consuming. On the contrary, the widely applied Taguchi optimization method is faster with comparable effectiveness in certain optimization problems. This study explores the abilities of both methods within the optimization of a permanent magnet coupling, where the optimization objectives are the minimization of coupling volume and maximization of transmitted torque. The optimal geometry of the coupling and the obtained characteristics achieved by both methods are nearly identical. The magnetic torque density is enhanced by more than 20%, while the volume is reduced by 17%. Yet, the Taguchi method is found to be more time-efficient and effective within the considered optimization problem. Thanks to the additive manufacturing techniques, the initial design and the sophisticated geometry of the Taguchi optimal designs are precisely fabricated. The performances of the coupling designs are validated using an experimental setup.

scholarly journals A Method for Integration of Preferences to a Multi-Objective Evolutionary Algorithm Using Ordinal Multi-Criteria Classification

2021 ◽  
Vol 26 (2) ◽  
pp. 27
Author(s):  
Alejandro Castellanos-Alvarez ◽  
Laura Cruz-Reyes ◽  
Eduardo Fernandez ◽  
Nelson Rangel-Valdez ◽  
Claudia Gómez-Santillán ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Selective Pressure ◽  
Optimization Problems ◽  
Region Of Interest ◽  
Decision Makers ◽  
Multiple Objective ◽  
Objective Functions ◽  
Multi Objective ◽  
Imperfect Knowledge ◽  
Real World Problems

Most real-world problems require the optimization of multiple objective functions simultaneously, which can conflict with each other. The environment of these problems usually involves imprecise information derived from inaccurate measurements or the variability in decision-makers’ (DMs’) judgments and beliefs, which can lead to unsatisfactory solutions. The imperfect knowledge can be present either in objective functions, restrictions, or decision-maker’s preferences. These optimization problems have been solved using various techniques such as multi-objective evolutionary algorithms (MOEAs). This paper proposes a new MOEA called NSGA-III-P (non-nominated sorting genetic algorithm III with preferences). The main characteristic of NSGA-III-P is an ordinal multi-criteria classification method for preference integration to guide the algorithm to the region of interest given by the decision-maker’s preferences. Besides, the use of interval analysis allows the expression of preferences with imprecision. The experiments contrasted several versions of the proposed method with the original NSGA-III to analyze different selective pressure induced by the DM’s preferences. In these experiments, the algorithms solved three-objectives instances of the DTLZ problem. The obtained results showed a better approximation to the region of interest for a DM when its preferences are considered.

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