Weighted Fitness Function Research Articles

Entropy parameter optimization for epileptic seizure detection: A parallel approach

Brain Computer Interface (BCI) – one of the recent advancements in the field of Bioinformatics which offers a real-time support for the people, who are affected by chronic neurological disorders. Owing to the rapid progression of Electroencephalogram (EEG) – based BCI system, the detection of epileptic seizures has become much simpler. However, accurate detection through visual inspection is tedious, time-consuming and prone to error. Thus, automation has become inevitable and for automating the epileptic seizure detection, entropies are appropriate as the nature of EEG signals are complex, arrhythmic, ephemeral, and non-stationary. Several renowned entropies are widely applied, nevertheless, the existing models fail to identify the optimal parameters of the entropies which greatly influences the performance of the Machine Learning models that could make better predictions. Hence to address the aforementioned issue, this paper presents a parallel machine learning based farmland fertility algorithm which optimizes the parameters of various entropies thereby detecting Epileptic Seizures in a systematic way. A novel weighted fitness function has been designed based on Kullback-Leibler Divergence (KLD). The extracted features are further classified using state-of-the-art classifiers. The overall performance of the proposed algorithm was evaluated using the EEG dataset obtained from University of Bonn, Germany, University of Bern and Indian EEG, New Delhi and the results show the supremacy of the proposed model in terms of sensitivity, specificity, precision, F1-score, G-mean and classification accuracy.

Synthesising Diverse and Discriminatory Sets of Instances using Novelty Search in Combinatorial Domains.

Gathering sufficient instance data to either train algorithm-selection models or understand algorithm footprints within an instance space can be challenging. We propose an approach to generating synthetic instances that are tailored to perform well with respect to a target algorithm belonging to a predefined portfolio but are also diverse with respect to their features. Our approach uses a novelty search algorithm with a linearly weighted fitness function that balances novelty and performance to generate a large set of diverse and discriminatory instances in a single run of the algorithm. We consider two definitions of novelty: (1) with respect to discriminatory performance within a portfolio of solvers; (2) with respect to the features of the evolved instances. We evaluate the proposed method with respect to its ability to generate diverse and discriminatory instances in two domains (knapsack and bin-packing), comparing to another well-known quality diversity method, Multi-dimensional Archive of Phenotypic Elites (MAP-Elites) and an evolutionary algorithm that only evolves for discriminatory behaviour. The results demonstrate that the novelty search method outperforms its competitors in terms of coverage of the space and its ability to generate instances that are diverse regarding the relative size of the "performance gap" between the target solver and the remaining solvers in the portfolio. Moreover, for the Knapsack domain, we also show that we are able to generate novel instances in regions of an instance space not covered by existing benchmarks using a portfolio of state-of-the-art solvers. Finally, we demonstrate that the method is robust to different portfolios of solvers (stochastic approaches, deterministic heuristics and state-of-the-art methods), thereby providing further evidence of its generality.

Application of GA-WELM Model Based on Stratified Cross-Validation in Intrusion Detection

Aiming at the problem of poor detection performance under the environment of imbalanced type distribution, an intrusion detection model of genetic algorithm to optimize weighted extreme learning machine based on stratified cross-validation (SCV-GA-WELM) is proposed. In order to solve the problem of imbalanced data types in cross-validation subsets, SCV is used to ensure that the data distribution in all subsets is consistent, thus avoiding model over-fitting. The traditional fitness function cannot solve the problem of small sample classification well. By designing a weighted fitness function and giving high weight to small sample data, the performance of the model can be effectively improved in the environment of imbalanced type distribution. The experimental results show that this model is superior to other intrusion detection models in recall and McNemar hypothesis test. In addition, the recall of the model for small sample data is higher, reaching 91.5% and 95.1%, respectively. This shows that it can effectively detect intrusions in an environment with imbalanced type distribution. Therefore, the model has practical application value in the field of intrusion detection, and can be used to improve the performance of intrusion detection systems in the actual environment. This method has a wide application prospect, such as network security, industrial control system, and power system.

Optimal placement method of multi-objective and multi-type sensors for courtyard-style timber historical buildings based on Meta-genetic algorithm

Optimal sensor placement for timber architecture heritage poses a significant challenge due to the unique structural types and complex monitoring purposes. In this study, a three-stage method is proposed, taking a courtyard-style heritage, built 133 years ago, as an example. First, a finite element model that accounted for the parameter randomness and initial damage was constructed using a genetic algorithm (GA) and experimental results. Second, a new weighted fitness function of logarithmic type was developed for multi-type sensors and multi-objective monitoring. Third, a novel genetic algorithm, Meta-GA, was proposed, introducing competition group mechanisms and gene libraries to improve optimal capability while maintaining computational efficiency. The Meta-GA is then compared to the other two optimization modes using seven indexes. Finally, damage detection capability was tested for the proposed three schemes at noise levels of 0%, 5%, and 10%. The results reveal that the proposed three-stage method with Meta-GA can provide the best solution.

A novel cluster head selection algorithm based IoT enabled heterogeneous WSNs distributed architecture for smart city

The Internet of Things (IoT) is a revolutionary development that facilitates the instant communication of data among devices, making it an essential paradigm in today's world. It enables the development of smart infrastructure and provides assistance in emergency services during disasters. However, there is a growing need to develop energy-conserving paradigms that cover all aspects of a smart city. In this work, the authors present a hybrid genetic algorithm (GA) inspired greedy strategy-based mutation operation for IoT-enabled heterogeneous wireless sensor networks (WSNs) distributed architecture in a technology driven city. The proposed protocol aims to design a weighted fitness function that considers four parameters: node density, residual and average energy, and distance parameters. Additionally, a 3-tier heterogeneity is considered to prolong the network functional duration, and a deployment strategy is addressed to position these nodes in an energy-efficient manner. The proposed algorithm achieves 31.41%, 20.42%, 13.37%, 1.59% increase in network lifespan compared to GA-based optimized clustering (GAOC), optimized GA with single sink (OptiGAS-StSS), multiple sinks (MS) based GAOC and optimized GA with multiple sink (OptiGAS-StMS), respectively. The significant enhancement in network lifespan is an outcome of choosing the most efficient CH nodes based on the proposed fitness function. Overall, the proposed protocol contributes to developing an energy-conserving paradigm for a smart city.

Steering the interpretability of decision trees using lasso regression - an evolutionary perspective

Since machine and deep learning have made accurate solutions possible, the search for explainable predictors has begun. Decision trees are competitive in tasks that require transparency, but have been underestimated due to their insufficient prediction performance, often caused by generalization issues. It is especially noticeable in the case of model trees, designed to solve regression tasks. Evolutionary tree induction can to some extent counteract this over and under-fitting problem with its global approach.In this paper, we examine whether integrating the lasso estimator in the tree induction process, can help to control the interpretability of the decision tree and/or improve its overall performance. We propose a new evolutionary model tree inducer called Global Lasso Tree. Its novelty lies in regularization of linear models coefficients, in the leaves during the evolutionary search. To reduce the tree's tendency to misfit, a weighted fitness function is used to dynamically balance the trade-off between conflicting objectives which is the tree error and overall complexity. The proposed method was validated on 26 publicly available regression data sets. The empirical study showed that by using the lasso-based regularization technique, we were able to steer the tree's interpretability and thus generate simpler and significantly more accurate trees.

An Improved Adaptive Coordination Control of Wind Integrated Multi-Terminal HVdc System

The increasing penetration of the renewables and integration of the power electronic devices leads to lower system inertia, which is changeling the system stability of a multiterminal HVdc (MTdc) system. This article presents an improved adaptive predictive control with the multiobjective targets coordinating the key parameters that the dc voltage, ac frequency and power-sharing among the terminals in MTdc system. Specifically, we contribute two main points to the relevant literature, with the purpose of distinguishing our study from existing ones. First, the proposed method is based on minimal information exchange by only considering neighboring terminals. Second, the adaptive control is achieved by setting a weighted fitness function to adaptively tune the weights with the effective integration of the trust-region and particle swarm optimization. A four-terminal HVdc system built within the IEEE 30-bus ac system is used as the study case to validate the robustness and efficiency of the proposed method. In the case study regarding the multiobjective fitness function, the proposed approach benefits in suppressing the voltage deviation, providing frequency support and establishing an automatically updated power equilibrium leveraging by the adaptive parameters.

CBA-CLSVE: A Class-Level Soft-Voting Ensemble Based on the Chaos Bat Algorithm for Intrusion Detection

Various machine-learning methods have been applied to anomaly intrusion detection. However, the Intrusion Detection System still faces challenges in improving Detection Rate and reducing False Positive Rate. In this paper, a Class-Level Soft-Voting Ensemble (CLSVE) scheme based on the Chaos Bat Algorithm (CBA), called CBA-CLSVE, is proposed for intrusion detection. The Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Decision Tree (DT) are selected as the base learners of the ensemble. The Chaos Bat Algorithm is used to generate class-level weights to create the weighted voting ensemble. A weighted fitness function considering the tradeoff between maximizing Detection Rate and minimizing False Positive Rate is proposed. In the experiments, the NSL-KDD, UNSW-NB15 and CICIDS2017 datasets are used to verify the scheme. The experimental results show that the class-level weights generated by CBA can be used to improve the combinative performance. They also show that the same ensemble performance can be achieved using about half the total number of features or fewer.

Empirical analyses of genetic algorithm and grey wolf optimiser to improve their efficiency with a new multi-objective weighted fitness function for feature selection in machine learning classification: the roadmap

ABSTRACT Feature selection (FS) is an optimisation problem that reduces the dimension of the dataset and increases the performance of the machine learning algorithms and classification through the selection of the optimal subset features and elimination of the redundant features. However, the huge search space is an important challenge in the FS problem. Due to their satisfactory capabilities to handle high-dimension search spaces, meta-heuristic search algorithms have recently gained much attention and become popular in the FS problem. For these algorithms, choosing a proper fitness function plays an important role. The fitness function orients the searching strategy of the algorithms to obtain best solutions. Appropriate fitness functions will help the algorithms with exploring the search space more effectively and efficiently. In this work, firstly the efficiency of two of the most outstanding and successful heuristic algorithms in the FS domain, namely genetic algorithm (GA) and grey wolf optimiser (GWO), are investigated and analysed with a single-objective fitness function. Secondly, two recent feature selection techniques based on GA and GWO, namely feature selection, weight, and parameter optimisation (FWP) and binary GWO (BGWO) with their fitness function are investigated and analysed. Thirdly, in order to remove the detected drawbacks and weaknesses of the FS algorithms and to enhance their efficiency, a new multi-objective weighted fitness function based on multiple predominant criteria has been presented. The effectiveness of the proposed fitness function on the FS algorithms is evaluated by using SVM and associative classification on 11 different large and small datasets. The experimental results show the superiority of proposed fitness function (where features were reduced and the classification performance has been improved) over single-objective fitness function and other existing fitness functions. Furthermore, another key aim of this study is to present a comprehensive study about the strengths and weaknesses of the FS algorithms which can be used as guidelines for future possible works to more improve the developments of these algorithms.

Intelligent Framework Using IoT-Based WSNs for Wildfire Detection

IoT-based WSNs have proved their significance in delivering critical information pertaining to hostile applications such as Wildfire Detection (WD) with the least possible delay. However, the sensor nodes deployed in such networks suffer from the perturbing concern of limited energy resources, restricting their potential in the successful detection of wildfire. To extenuate this concern, we propose an intelligent framework, Sleep scheduling-based Energy Optimized Framework (SEOF), that works in two folds. Firstly, we propose an energy-efficient Cluster Head (CH) selection employing a recently developed meta-heuristic method, Tunicate Swarm Algorithm (TSA), that optimizes the five novel fitness parameters by integrating them into its weighted fitness function. Secondly, we perform a sleep scheduling of closely-located sensor nodes based on the distance threshold calculated through a set of experiments. Sleep scheduling methodology plays a pivotal role in abating the number of data transmissions in SEOF. Finally, we simulate SEOF in MATLAB under different scenarios to examine its efficacy for the various performance metrics and scalability features. Our empirical results prove that SEOF has ameliorated the network stability period for two different scenarios of network parameters by 35.3% and 216% vis-a-vis CIRP.

A novel objective function with artificial ecosystem-based optimization for relieving the mismatching power loss of large-scale photovoltaic array

Harvesting maximum power from a partially shaded photovoltaic array is a critical issue that attracts the attention of several researchers. As per the literature, it is found that providing an optimal reconfigured pattern of the shaded photovoltaic array is an optimal solution for this issue. Therefore, in this paper, an innovative fitness function has been considered with the artificial ecosystem-based optimization for an electrical photovoltaic array reconfiguration approach. The proposed approach has been applied for the large scale photovoltaic arrays including 9 ×9,6×20,16×16, and 25 × 25 photovoltaic array with different shade patterns. The new fitness function has been validated via a comparison with the regular used weighted function in literature. The quality of the solutions of the proposed artificial ecosystem-based optimization–reconfiguration approach has been assessed and demonstrated via performing several measures namely fill factor, percentage of power loss, mismatch power loss, and power enhancement in comparison with a total cross-tied, particle swarm optimizer approaches, and harris hawks optimizer. Furthermore, the Wilcoxon signed-rank test has been performed to illustrate the applicability, robustness, and consistency of the proposed algorithm results across several independent runs. The analysis reveals the quality of the innovative fitness function while integrating with the optimization algorithms in comparison to the weighted fitness function in producing higher power values via attaining a more efficient photovoltaic array design. Furthermore, the results confirmed the efficiency of the artificial ecosystem-based optimization–photovoltaic reconfiguration approach in boosting the generated photovoltaic power by a percentage of 28.688%, 7.0197 %, 29.2565%, 8.3811% and 5.3884 % across the considered systems with an uniform dispersion of the shadow on the photovoltaic surface and providing highest consistent in the maximum power values across the independent runs.

A novel multi-objective genetic algorithm approach to address class imbalance for disease diagnosis

The adverse consequences of class imbalance problem are prevalent while performing classification for disease diagnosis. Such classifiers predict most of the examples of negative class (instances with non-diseased label) correctly but fail to make correct predictions for the positive class examples (instances with diseased label). Since misclassification costs can be very high for a sensitive field like disease diagnosis, addressing the class imbalance issue becomes of utmost important. A number of sampling techniques have been applied for balancing data. However, these techniques reduce the overall accuracy of classifier models. This is due to the fact that there is an issue of trade-off between sensitivity and specificity of such classifiers. This paper first proposes a GA-based undersampling technique with a weighted fitness function to determine the trade-off between sensitivity and specificity followed by a multi-objective genetic algorithm (MOGA) approach to address the class imbalance problem for disease diagnosis. To determine the trade-off between sensitivity and specificity manually is an arduous task. The MOGA approach takes the two extreme training samples from Pareto optimal solutions, one optimally tuned with respect to sensitivity and the other one optimally tuned with respect to specificity on validation data. Two decision tree classification models are built based on these two training sets. The models are named as sensitivity prioritized model (SEPM) and specificity prioritized model (SPPM) respectively. These models are combined to make predictions on the test data. The results obtained through extensive experimentation confirm that the proposed multi-objective scheme makes correct predictions on minority class (SE) without compromising the correct prediction rate on the majority class (SP).

Multiobjective scheduling algorithm for flexible manufacturing systems with Petri nets

In this work, we focus on general multi-objective scheduling problems that can be modeled using a Petri net framework. Due to their generality, Petri nets are a useful abstraction that captures multiple characteristics of real-life processes.To provide a general solution procedure for the abstraction, we propose three alternative approaches using an indirect scheme to represent the solution: (1) a genetic algorithm that combines two objectives through a weighted fitness function, (2) a non dominated sorting genetic algorithm (NSGA-II) that explicitly addresses the multi-objective nature of the problem and (3) a multi-objective local search approach that simultaneously explores multiple candidate solutions. These algorithms are tested in an extensive computational experiment showing the applicability of this general framework to obtain quality solutions.

Conic sections fitting in disperse data using Differential Evolution

Robust conics detection and extraction have received an increased interest due to the potential applications in many critical tasks. In medical imaging, the conics can help to detect optic disk abnormalities in retinographic images, or cranial sections and bone structures in radiography, magnetic resonance, and computed tomography images. Moreover, the physicians can be guided in the prostate cancer diagnosis by assessing the size and shape of the prostate in ultrasound or magnetic resonance images. Some of these structures are composed of discontinuous points, imprecise directions, multiple bifurcations, and variable line–trace width, or may present undesired noise, which makes the curve fitting hard to compute. Image acquisition also plays a fundamental role in adding artifacts, as unexpected outliers, which jeopardizes the applicability and efficiency of the current methods. This article proposes a strategy for determining the general equation of oblique conics in noisy and sparse data sets. As a novelty, this methodology employs four uncorrelated points from a randomly sampled population to estimate the initial space search. Then, Differential Evolution (DE) method iterates until the solution that best fits the oblique conic section is found. Advantages of the method include a DE implementation using a weighted fitness function based on the Mean Squared Error (MSE) and reinforced with the Random Sample Consensus (RANSAC) inliers measurement idea. This objective function allows associating the fitting error to the number of points into the inliers-region. Additionally, the number of parameters to tune is reduced and almost generalized. A distinctive feature is related to the eccentricity, which could conduct to a variety of rich solutions. However, some disadvantages have arisen trying to identify a specific conic. For instance, when some parabolas are considered, the method could evolve optimally to an ellipse. The proposed methodology has been evaluated using synthetic and real images. These images were contaminated with additive white Gaussian noise to prove the method stability. Numerical results showed that the proposed method is a reliable and fast technique for computing any conic from scattered data. Results obtained from two dissimilar applications prove that the proposed method can be used in a variety of sensitive applications. Those applications include the optic disk segmentation and traffic sign classification. The assessment showed an improvement of 2% across the metrics for detecting the optic disk and reached a classification accuracy of 0.9730 for the speed limit traffic signs.

The Determination of Preventive Maintenance using Simulated Annealing Algorithm based on Weighted Fitness Function

This study aims to determine the machine maintenance schedule. We use the Simulated Annealing Algorithm. Fitness and reliability functions are functions that are used in the optimization process. Several weighting scenarios are done to see the unity of the function. The results of the scenario produce several alternative schedules. This algorithm is implemented on machines that have more than one sub-machine. This sub-machine is a smaller engine system part. This sub-machine also has one particular function. The results of the study show that the optimal engine maintenance period to use is six periods. There are five scheduling scenarios used in this problem. The resulting schedule can increase the value of reliability and can minimize costs.

WallSense: Device-Free Indoor Localization Using Wall-Mounted UHF RFID Tags.

To achieve device-free indoor localization without the active participation of the users, this paper presents WallSense, a device-free indoor localization system based on off-the-shelf Radio RFID (Radio-Frequency Identification) equipment. The system deploys two orthogonal tag arrays in adjoining walls and uses the RSSI and phase information measured by RFID readers to localize the target. By differentiating the backscattered signal between adjacent tag pairs, WallSense is able to eliminate most undesirable factors and extract information directly related to the location of the target. By applying Particle Swarm Optimization (PSO) with a novel weighted fitness function and combining the localization result of two orthogonal tag arrays, the system is able to localize the target with high accuracy. Experiments show that the system is able to localize human target with 0.24 m median error. Also, WallSense has low deployment overhead and do not require the users to carry any devices.

Reflecting metallic metasurfaces designed with stochastic optimization as waveplates for manipulating light polarization

We demonstrate that a stochastic optimization algorithm with a properly chosen, weighted fitness function, following a global variation of parameters upon each step can be used to effectively design reflective polarizing optical elements. Two sub-wavelength metallic metasurfaces, corresponding to broadband half- and quarter-waveplates are demonstrated with simple structure topology, a uniform metallic coating and with the design suited for the currently available microfabrication techniques, such as ion milling or 3D printing.

Emergence nonlinear Multifractal architecture by Hypervolume estimation algorithm for evolutionary multi-criteria optimisation

Currently, architecture typologies and urban morphogenesis are subject to the enormous diversification of emergence mechanisms, in addition to an accelerated pace of computational approaches, seeking optimum performance competitively. Whilst environmental, material behaviour, biological design and a broad spectrum of linear form finding processes are incapable, alone, of identifying between the current complex morphology, co-evolving and the human bio-functional interrelation evolution. Only a new language grounded on Artificial Live evolution of forms, based on different methods such as Multifractal design, chaos theory, nonlinear dynamics and complexity theory could permeate and adapt to the huge scientific challenges, with continuous mutations and self-organization. The focus of this research is in the presentation of an evolutionary Multifractal architecture method, able to generate patterns that adaptively self-organize depending on the feedback, by using a Hypervolume multi-criteria optimisation estimation algorithm, based on weighted fitness function design. This convergence between the science of complexity with architecture and urban morphology forms a starting point from which the creation of both integrative and innovative architecture and urban design processes arise.

Multi-subpopulation evolutionary algorithms for coverage deployment of UAV-networks

The deployment of an unmanned aerial network (UAV-network) for the optimal coverage of ground nodes is an NP-hard problem. This work focuses on the application of a multi-layout multi-subpopulation genetic algorithm (MLMPGA) to solve multi-objective coverage problems of UAV-networks. The multi-objective deployment is based on a weighted fitness function that takes into account coverage, fault-tolerance, and redundancy as relevant factors to optimally place the UAVs. The proposed approach takes advantage of different subpopulations evolving with different layouts. This feature is aimed at reflecting the evolutionary concept of different species adapting to the search space conditions of the multi-objective coverage problem better than single-population genetic algorithms. The proposed multi-subpopulation genetic algorithm is evaluated and compared against single-population genetic algorithm configurations and other well-known meta-heuristic optimization algorithms, such as particle swarm optimization and hill climbing algorithm, under different numbers of ground nodes. The proposed MLMPGA achieves significantly better performance results than the other meta-heuristic algorithms, such as classical genetic algorithms, hill climbing algorithm, and particle swarm optimization, in the vast majority of our simulation scenarios.

A tabu search based approach for graph layout

This paper describes an automated tabu search based method for drawing general graph layouts with straight lines. To our knowledge, this is the first time tabu methods have been applied to graph drawing. We formulated the task as a multi-criteria optimization problem with a number of metrics which are used in a weighted fitness function to measure the aesthetic quality of the graph layout. The main goal of this work is to speed up the graph layout process without sacrificing layout quality. To achieve this, we use a tabu search based method that goes through a predefined number of iterations to minimize the value of the fitness function. Tabu search always chooses the best solution in the neighbourhood. This may lead to cycling, so a tabu list is used to store moves that are not permitted, meaning that the algorithm does not choose previous solutions for a set period of time. We evaluate the method according to the time spent to draw a graph and the quality of the drawn graphs. We give experimental results applied on random graphs and we provide statistical evidence that our method outperforms a fast search-based drawing method (hill climbing) in execution time while it produces comparably good graph layouts. We also demonstrate the method on real world graph datasets to show that we can reproduce similar results in a real world setting.