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The main focus of this paper is on the family of evolutionary algorithms and their real-life applications. We present the following algorithms: genetic algorithms, genetic programming, differential evolution, evolution strategies, and evolutionary programming. Each technique is presented in the pseudo-code form, which can be used for its easy implementation in any programming language. We present the main properties of each algorithm described in this paper. We also show many state-of-the-art practical applications and modifications of the early evolutionary methods. The open research issues are indicated for the family of evolutionary algorithms. [ABSTRACT FROM AUTHOR]

Scheduling complex applications as task graphs on finite computational resources assuring task interdependencies is a well-known NP-complete optimization problem. This problem is well-addressed for microprocessor systems but for Dynamically Reconfigurable Hardware (DRHW) systems in which, in addition to tasks, the reconfiguration time and complexity also have to be scheduled; this problem is more complicated. DRHW reconfiguration overhead is considerable and can be crucial for real-world applications. To deal with this overhead, in this paper, a meta-heuristic method named Graph-Oriented Simulated Annealing (GOSA) is proposed. By introducing an innovative graph and solution structures called schedule graphs, and also some controlling functions which are inherited from the nature of the problem, the proposed method adapts itself to the characteristics of the problem. This helps the algorithm to adjust its exploration and exploitation speed and accuracy according to the requirements of the given problem and consequently find high-quality solutions quickly. To demonstrate the performance of the proposed method, it was tested on several synthetic and real-world benchmark task graphs, and the results were compared with a selection of classic and state-of-the-art algorithms. The method is comprehensively evaluated by performing numerous experiments in terms of execution time, makespan, scalability, and reliability. The results of the experiments on benchmarks show that in terms of the quality of the solutions, GOSA outperforms BGA, HPSO-GA, and FATS by 17%, 13%, and 5%, respectively, and its execution time is considerably less than all competing algorithms. Moreover, according to the experiments done on synthetic graphs, the makespan of the solutions generated by GOSA, Genetic Algorithm (GA), and the Gxhaustive Search over the List Scheduler are improved on average by 7.2%, 8.1%, and 19.1%, respectively. The most significant achievement of the proposed method is its execution time which is 31 times faster than GA. Finally, the results confirm that the proposed method is scalable for large task graphs, and its reliability is superior. [ABSTRACT FROM AUTHOR]

This paper proposes the multi-objective variant of the recently-introduced fitness dependent optimizer (FDO). The algorithm is called a multi-objective fitness dependent optimizer (MOFDO) and is equipped with all five types of knowledge (situational, normative, topographical, domain, and historical knowledge) as in FDO. MOFDO is tested on two standard benchmarks for the performance-proof purpose: classical ZDT test functions, which is a widespread test suite that takes its name from its authors Zitzler, Deb, and Thiele, and on IEEE Congress of Evolutionary Computation benchmark (CEC-2019) multi-modal multi-objective functions. MOFDO results are compared to the latest variant of multi-objective particle swarm optimization, non-dominated sorting genetic algorithm third improvement (NSGA-III), and multi-objective dragonfly algorithm. The comparative study shows the superiority of MOFDO in most cases and comparative results in other cases. Moreover, MOFDO is used for optimizing real-world engineering problems (e.g., welded beam design problems). It is observed that the proposed algorithm successfully provides a wide variety of well-distributed feasible solutions, which enable the decision-makers to have more applicable-comfort choices to consider. [ABSTRACT FROM AUTHOR]

This paper addresses a new multi-level serial closed-loop supply chain (CLSC) model with an integrated formulation of batch deliveries, quality-dependent return rate, random defective rate, rework process, and learning effects. The vendor managed inventory contract has motivated companies to cope with the bullwhip effect and its associated shortages. However, getting precise information on manufacturing/remanufacturing time is a challenge owing to human learning. Accordingly, this paper tries to incorporate the learning effect on the inventory control problem. This is a bridge to fill the gaps in the literature by considering how the learning process affects the production time and system costs in a CLSC problem. Moreover, the mean-standard deviation utility function is used in order to take the random defective rate into account, to minimize mean system costs with respect to the standard deviation. Due to the complexity of the proposed mixed-integer nonlinear programming model, three metaheuristic algorithms named, genetic algorithm, invasive weed optimization algorithm, and moth flame optimization algorithm are used to solve the problem, and Fibonacci algorithm will examine the validity of the applied methods. Two sets of numerical examples in small, medium, and large sizes are presented to illustrate the performance of the algorithms in terms of objective function values and required CPU-time. Afterward, a statistical analysis is utilized in order to evaluate the proposed algorithms. Finally, the results show how significantly the learning effect in manufacturing/remanufacturing time affects the costs of CLSC problem. [ABSTRACT FROM AUTHOR]

In this paper, considering the applications of interval analysis in various fields (such as artificial intelligence, neural computation, genetic algorithms, information theory or fuzzy logic), a new class of interval-valued variational control problems governed by multiple integral functionals, first-order PDE and inequality constraints is studied. More precisely, efficiency conditions for the considered uncertain variational control problem are formulated and proved. The sufficiency of Karush–Kuhn–Tucker conditions is established under some invexity and (ρ , b) -quasiinvexity assumptions of the involved functionals. In addition, the paper is completed with illustrative applications (describing the controlled behavior of an artificial neural system) and the corresponding algorithm. [ABSTRACT FROM AUTHOR]

The chimp optimization algorithm (ChOA) is a nature-inspired algorithm that imitates chimpanzees' individual intelligence and hunting behaviors. In this algorithm, the hunting process consists of four steps: driving, blocking, chasing, and attacking. Because of the novelty of ChOA, the steps of the hunting process have been modeled in the simplest possible way, leading to slow and premature convergence similar to other iterative algorithms. This paper proposes six spiral functions and introduces two novel hybrid spiral functions (SEB-ChOA) to rectify the abovementioned deficiencies. The SEB-ChOAs' performance is evaluated on 23 standard benchmarks, 20 benchmarks of IEEE CEC-2005, 10 cases of IEEE CEC06-2019 test-suite, and 12 constrained real-world engineering problems of IEEE CEC-2020. The SEB-ChOAs are compared with three groups of optimization algorithms, including Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) as the most well-known optimization algorithms, Slime Mould Algorithm (SMA), Marine Predators Algorithm (MPA), Ant Lion Optimization (ALO), Henry Gas Solubility Optimization (HGSO), as almost novel optimization algorithms, and jDE100 and DISHchain1e+12, as winners of IEEE CEC06-2019 competition, and also EBOwithCMAR and CIPDE as superior secondary optimization algorithms. The SEB-ChOAs reached the first rank among almost all benchmarks and demonstrated very competitive results compared to jDE100 and DISHchain1e+12 as the best-performing optimizers. Statistical evidence shows that the SEB-ChOA outperforms the PSO, GA, SMA, MPA, ALO, and HGSO optimizers while producing results comparable to those of the jDE100 and DISHchain1e+12 algorithms. [ABSTRACT FROM AUTHOR]

In this paper, an image encryption algorithm based on Fibonacci Q-matrix and genetic algorithm is proposed. The new four-layer encryption framework of diffusion-scrambling-diffusion-optimization is adopted. Firstly, an improved four-direction diffusion operation is performed on the original image to achieve efficient global diffusion based on row and column vectors and the first-round diffusion matrix is obtained. Secondly, using the Logistic chaotic system to improve the Josephus scrambling, and then use the improved scrambling method to scramble the first-round diffusion matrix, which improves the scrambling effect of the algorithm. Thirdly, the scrambling matrix is divided into blocks, and then the Fibonacci Q-matrix corresponding to each block is dynamically selected by random numbers and the two are rapidly diffused, which further improves the diffusion effect. Finally, in order to make the optimization more targeted, the initial population is generated by the second-round diffusion matrix and the fitness function is selected according to the correlation of the second-round diffusion matrix. In the optimization process, multiple rounds of selection, crossover and fitness calculation are performed on the initial population to obtain the encrypted image. In addition, the initial values of the NHS hyperchaotic system used in the improved four-way diffusion and the Logistic chaotic system used in the improved Josephus scrambling is all generated from the 256-bit hash values of the original image, which makes the proposed algorithm highly sensitive to plaintext image. The experimental results and security analyses show that the algorithm not only has high security but also has certain robustness and real-time performance, which is suitable for practical applications. [ABSTRACT FROM AUTHOR]

Simulation optimization studies the problem of optimizing simulation-based objectives. This field has a strong history in engineering but often suffers from several difficulties including being time-consuming and NP-hardness. Simulation optimization is a new and hot topic in the field of system simulation and operational research. This paper presents a hybrid approach that combines Evolutionary Algorithms with neural networks (NNs) for solving simulation optimization problems. In this hybrid approach, we use NNs to replace the known simulation model for evaluating subsequent iterative solutions. Further, we apply the dynamic structure-based neural networks to learn and replace the known simulation model. The determination of dynamic structure-based neural networks is the kernel of this paper. The final experimental results demonstrated that the proposed approach can find optimal or close-to-optimal solutions and is superior to other recent algorithms in simulation optimization. [ABSTRACT FROM AUTHOR]

The minimum vertex cover problem (MVCP) is one of the well-known NP-complete problems that can be used to formulate numerous real-life applications. The MVCP has been solved using different approaches, exact, heuristic, and metaheuristic. Chemical reaction optimization (CRO) is a population-based metaheuristic algorithm that simulates what happens in chemical reactions to solve many problems. In this paper, the MVCP is solved using hybridization of a modified version of the CRO algorithm and the best-first search (BFS) algorithm. This hybridization is symbolized by MCROA-BFS. The BFS algorithm is exploited to generate initial populations with high-quality initial solutions in comparison with the random bit-vector (RBV) approach as a traditional approach for generating initial populations for several population-based metaheuristic algorithms. At first, the MCROA is evaluated analytically in terms of run time complexity. Then the performance of the MCROA is evaluated experimentally to show the effectiveness of exploiting the BFS in terms of quality of gained solutions and run time. The most valuable player algorithm (MVPA), genetic algorithm (GA), and hybrid CRO algorithm (HCROA) are metaheuristic algorithms that used the RBV approach to generate the initial population. MCROA-BFS is compared, under the selected performance metrics, with these metaheuristic algorithms in addition to MCROA with the RBV approach (MCROA-RBV). The conducted experiments were carried out using 18 instances of DIMACS and BHOSLIB benchmarks. The experimental results revealed the superiority of MCROA-BFS over MVPA, GA, and MCROA-RBV in terms of both performance metrics. Although HCROA has the smallest run time, it failed to obtain high-quality solutions in comparison with MCROA. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms are generally a suitable approach for optimization problems, having more than one conflicting objectives. For many complicated engineering optimization problems, multi-objective formulations are treated as realistic models. The paper presents and implements a Pareto-optimal image encryption algorithm that uses coupled map lattice (CML) chaos function and deoxyribonucleic acid (DNA) combination to encrypt an image. The discussed work uses multi-objective genetic algorithm (MOGA) to get the optimized results. The proposed two-step algorithm uses pseudo-random number generators, the chaotic method CML and DNA to create an initial population of DNA masks in its initial stage. The MOGA is applied in the second stage to obtain the best mask for encrypting the given plain image. The focus is on the generation of Pareto fronts by using the Pareto generation method of multi-objective optimization. The paper evaluates the performance of the implemented work using standard metrics like key sensitivity, secret key space, number of pixel change rate, unified average changed intensity, entropy, histogram and correlation coefficient. It also discusses the impact of using a genetic algorithm that uses more than one fitness function as the objective for encrypting images. [ABSTRACT FROM AUTHOR]

Power system stabilizers (PSSs) are extensively used in generator units to enhance the transient stability of the power system. Hence, optimal tuning and placement of the parameters of PSS are crucial for the efficiency of the stabilizer. Researchers have been proposed many methods for optimizing such parameters. In this paper, the Crow search algorithm (CSA), which is based on the intelligence of crows, was employed in a single-machine infinite-bus (SMIB) system to determine the optimum parameters of the PSS. Modeling and simulation of the SMIB and designing of PSS were made by MATLAB/Simulink. PSSs are designed to minimize low-frequency oscillations such as power angle, rotor speed, and field current deviation following a large disturbance. Our objective in this study is the minimization of the rotor speed deviation. The results of the simulations proved the effectiveness and robustness of the optimization process compared to other metaheuristic algorithms such as particle swarm optimization (PSO) and genetic algorithm (GA). When compared to the performance attained by the GA-based and PSO-based PSS controller designs, the simulations show that the CSA-based PSS delivers a far better dynamic response when the system is disrupted. The CSA-based PSS settles 48.1% faster than the PSO-based PSS and 55.7% faster than the GA-based PSS. CSA has just 2 parameters to adjust, making it much easier to implement than other methods. These parameters for PSO and GA are 4 and 6. When CSA-based PSS is used in the SMIB system, overshoot and low-frequency oscillations are also significantly reduced compared to other methods. [ABSTRACT FROM AUTHOR]

Cloud providers deliver heterogeneous virtual machines to run complicated jobs submitted by users. The task scheduling issue is formulated to a discrete optimization problem which is well-known NP-Hard. This paper presents a hybrid meta-heuristic algorithm based on genetic and thermodynamic simulated annealing algorithms to solve this problem. In the proposed algorithm, the genetic and simulated annealing algorithms have respective global and local search inclinations covering each other's shortcomings. A novel theorem is presented and applied to produce a semi-conducted initial population. In a used genetic algorithm with a global trend, the crossover operator is performed to explore search space. The thermodynamic simulated annealing algorithm is utilized to improve the efficiency, which considers entropy and energy difference concepts in the cooling schedule process. After obtaining a suitable solution, one of the three novel neighbor operators is randomly called to enhance the given solution potentially. In this way, the efficient balance between exploration and exploitation in the search space is achieved. Simulation results prove that the proposed hybrid algorithm has 10.17%, 9.31%, 7.76%, and 8.21% dominance in terms of makespan, schedule length ratio, speedup, and efficiency against other comparative algorithms. [ABSTRACT FROM AUTHOR]

The cloud computing systems are sorts of shared collateral structure which has been in demand from its inception. In these systems, clients are able to access existing services based on their needs and without knowing where the service is located and how it is delivered, and only pay for the service used. Like other systems, there are challenges in the cloud computing system. Because of a wide array of clients and the variety of services available in this system, it can be said that the issue of scheduling and, of course, energy consumption is essential challenge of this system. Therefore, it should be properly provided to users, which minimizes both the cost of the provider and consumer and the energy consumption, and this requires the use of an optimal scheduling algorithm. In this paper, we present a two-step hybrid method for scheduling tasks aware of energy and time called Genetic Algorithm and Energy-Conscious Scheduling Heuristic based on the Genetic Algorithm. The first step involves prioritizing tasks, and the second step consists of assigning tasks to the processor. We prioritized tasks and generated primary chromosomes, and used the Energy-Conscious Scheduling Heuristic model, which is an energy-conscious model, to assign tasks to the processor. As the simulation results show, these results demonstrate that the proposed algorithm has been able to outperform other methods. [ABSTRACT FROM AUTHOR]

With the rapid development of e-commerce, last-mile delivery optimization is important for reduction in logistics cost of e-business enterprises. However, the complex road network structure in various cities makes the last-mile delivery more difficult, which shows high research value. To this end, this paper proposes a special kind of share-a-ride problem (SARP), which uses online car-hailing as a carrier to transport passengers to the distribution centre and courier points to pick up and deliver parcels. The dynamics of the problem is described by designing the key points to update the delivery information; by using an improved genetic algorithm (GA), the problem is solved to realize the goal of minimizing the total cost of three participants (i.e. drivers, passengers and courier companies) and the time penalty costs. Eventually, through simulation example and comparison tests based on three sets of data of different scales, the economic applicability of the problem and the effectiveness of the algorithm are validated. [ABSTRACT FROM AUTHOR]

Grapes reception is a key process in wine production. The harvest days are extremely challenging days in managing the reception of the grapes, as the winery needs to deal with the non-uniform arrival of the grapes, while guaranteeing suppliers' satisfaction and wine quality. The best management of the resources of the suppliers (i.e., grapes and trucks) and winery (i.e., grain-tanks and pressing machines) must be ensured. In this paper, the underlying optimization problem for grape reception is solved by developing a genetic algorithm (GA) tailored for this specific challenge. The results of this algorithm are compared with a FIFO policy for a typical scenario that occurs on the harvest days of a real winery. Additionally, different scenarios are simulated to assess the validity and quality of the solutions found. The results show that, using modest computational resources, it is possible to achieve better solutions with the proposed GA. This allows for the algorithm to be used in real time, even whenever plant conditions change significantly (e.g., when a new truck arrives, when a machine fails). Furthermore, the trucks and grapes waiting time for the results using the developed GA are significantly smaller than the ones observed using a FIFO approach. [ABSTRACT FROM AUTHOR]

The dynamics identification and subsequent control of a nonlinear system is not a trivial issue. The application of a neural gas network that is trained with a supervised batch version of the algorithm can produce identification models in a robust way. In this paper, the neural model identifies each local transfer function, demonstrating that the local linear approximation can be done. Moreover, other parameters are analyzed in order to obtain a correct modeling. Furthermore, the algorithm is applied to control a nonlinear multi-input multi-output system composed of tanks. In addition, this plant is a coupled system where the manipulated input variables are influencing all the output variables. The aim of the work is to demonstrate that the supervised neural gas algorithm is able to obtain linear models to be used in a state space design scenario to control nonlinear coupled systems and guarantee a robust control method. The results are compared with the common approach of using a recurrent neural controller trained with a dynamic backpropagation algorithm. Regarding the steady-state errors in disturbance rejection, reference tracking and sensitivity to simple process changes, the proposed approach shows an interesting application to control nonlinear plants. [ABSTRACT FROM AUTHOR]

Feature selection and construction are important preprocessing techniques in data mining. They allow not only dimensionality reduction but also classification accuracy and efficiency improvement. While feature selection consists in selecting a subset of relevant feature from the original feature set, feature construction corresponds to the generation of new high-level features, called constructed features, where each one of them is a combination of a subset of original features. Based on these definitions, feature construction could be seen as a bi-level optimization problem where the feature subset should be defined first and then the corresponding (near) optimal combination of the selected features should be found. Motivated by this observation, we propose, in this paper, a bi-level evolutionary approach for feature construction. The basic idea of our algorithm, named bi-level feature construction genetic algorithm (BFC-GA), is to evolve an upper-level population for the task of feature selection, while optimizing the feature combinations at the lower level by evolving a follower population. It is worth noting that for each upper-level individual (feature subset), a whole lower-level population is optimized to find the corresponding (near) optimal feature combination (constructed feature). In this way, BFC-GA would be able to output a set of optimized constructed features that could be very informative to the considered classifier. A detailed experimental study has been conducted on a set of commonly used datasets with varying dimensions. The statistical analysis of the obtained results shows the competitiveness and the outperformance of our bi-level feature construction approach with respect to many state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Job shop scheduling problem is a well-known NP problem. It is limited by various conditions. As the scale of the problem increases, the difficulty of finding the optimal solution will increase. It is a difficult combination problem. Limited by the constraints of the actual production environment, how to effectively arrange the processing order of each part will directly affect the production efficiency, the appropriate production scheduling algorithm can correctly and effectively plan the enterprise resources and rationally arrange the processing order and processing time of the workpiece. Proper use of existing resources, by optimizing production scheduling instructions, to meet the basic requirements of production scheduling, in order to obtain the optimization of total production time, has important theoretical significance for the actual production of enterprises. In this paper, the mathematical model is abstracted on the basis of the production scheduling problem. According to the different parts of the same machine and the different processes of the same part, the corresponding processing time and waiting time are obtained. At the same time, the genetic algorithm is improved by genetic algorithm. A dynamic genetic operator based on the number of iterations is proposed, which further enhances the convergence performance and search ability of the genetic algorithm. Through the simulation of MATLAB simulation program, combined with the scheduling standard example, the performance analysis of different algorithms is carried out, the search efficiency of genetic algorithm is improved, the convergence performance of the algorithm is improved, and different optimization choices are obtained for different time weights. The operation results of the system meet the requirements of production scheduling, which proves the feasibility and practicability of the improved genetic algorithm. [ABSTRACT FROM AUTHOR]

This paper deals with the spherical traveling salesman problem. In this problem, all cities are located on the surface of a sphere and the cities must be visited exactly once in a tour. We propose a new and effective meta-heuristic algorithm with greedy behavior for solving this problem. The proposed algorithm is based on the discrete flower pollination algorithm, which is a bio-inspired meta-heuristic algorithm enhanced by order-based crossover, pollen discarding behavior and partial behaviors. To evaluate the proposed algorithm, it is compared with four effective existing algorithms (the genetic algorithm, two variants of the genetic algorithm and tabu search) on a set of available spherical traveling salesman instances. The results show the superiority of our algorithm in both solution quality and robustness of the solutions. [ABSTRACT FROM AUTHOR]

In this paper, genetic algorithm is used to help improve the tolerance of feedforward neural networks against an open .fault. The proposed method does not explicitly add any redundancy to the network, nor does it modify the training algorithm. Experiments show that it may profit the fault tolerance as well as the generalisation ability of neural networks. [ABSTRACT FROM AUTHOR]

The Sierpinski gasket fractal antenna is most popular structure in the domain of fractal antennas. This fractal antenna has multi-band performance, and hence, the design of this antenna for the desired frequencies is a challenging problem. The artificial intelligence tools like artificial neural networks, fuzzy logic systems, bio-inspired optimization techniques are appropriate to provide accurate design solution in such cases. In this paper, three most popular bio-inspired optimization algorithms: genetic algorithms, particle swarm optimization (PSO), and bacterial foraging optimization, have been proposed to solve the design issues of Sierpinski gasket pre-fractal antenna. Their performances are analyzed and are compared with the experimental results. A simplified expression for calculation of resonant frequency of Sierpinski gasket pre-fractal antenna is proposed and is used as the objective function. Finally, the effectiveness is compared on the basis of three different measures: mean absolute percentage error, the average time taken by the models to evaluate the results, and the coefficient of correlation. The results indicate that the PSO algorithm is most suitable for this type of antenna. [ABSTRACT FROM AUTHOR]

In the process of ultrasound elastic image detection of pulmonary nodules, due to various factors, the detection process of nodules will produce less sensitive and higher false positives, which will affect the detection accuracy of nodules. In order to improve the ultrasound-assisted diagnosis of pulmonary nodules, this study based on genetic algorithm and combined with fish-following algorithm image recognition technology to construct an improved algorithm based on genetic algorithm. In addition, this study combs and improves the algorithm through process design and sets up the simulation environment for algorithm simulation research. Finally, in order to verify the performance of the algorithm, the ultrasound image of the pulmonary nodule is analyzed by an example to obtain the processed recognition image. From the point of view of identification, the algorithm proposed in this study has certain clinical effects and can provide theoretical reference for subsequent related research. [ABSTRACT FROM AUTHOR]

The use of artificial neural networks (ANNs) models has grown considerably over the last decade. One of the difficulties in using ANNs is the fact that in most cases there are several numbers of input variables available. In the past, there was a tendency to use a large number of inputs in ANNs applications. This can have a number of detrimental effects on the network during training and it also requires a greater amount of data to efficiently estimate the connection weights. Additional inputs tend to increase the required time for training and the risk of the training algorithm becoming stuck in a local minimum. A large number of inputs also increases the risk of including spurious variables that merely increase the noise in the forecasts. Consequently, it is important to use an appropriate selection technique of the input variables in order to obtain the smallest number of independent inputs that are useful predictors for the system which is being researched. The aim of this paper is to review techniques that will allow the selection of appropriate model inputs based particularly on mutual information and genetic algorithms. [ABSTRACT FROM AUTHOR]

This paper investigates the use of a genetic algorithm (GA) to perform the large-scale triangular mesh optimization process. This optimization process consists of a combination of mesh reduction and mesh smoothing that will not only improve the speed for the computation of a 3D graphical or finite element model, but also improve the quality of its mesh. The GA is developed and implemented to replace the original mesh with a re-triangulation process. The GA features optimized initial population, constrained crossover operator, constrained mutation operator and multi-objective fitness evaluation function. While retaining features is important to both visualization models and finite element models, this algorithm also optimizes the shape of the triangular elements, improves the smoothness of the mesh and performs mesh reduction based on the needs of the user. [ABSTRACT FROM AUTHOR]

This paper proposes a new codebook generation algorithm for image data compression using a combined scheme of principal component analysis (PCA) and genetic algorithm (GA). The combined scheme makes full use of the near global optimal searching ability of GA and the computation complexity reduction of PCA to compute the codebook. The experimental results show that our algorithm outperforms the popular LBG algorithm in terms of computational efficiency and image compression performance. [ABSTRACT FROM AUTHOR]

This paper presents an application of a hybrid neural network structure to the classification of the electrocardiogram (ECG) beats. Three different feature extraction methods are comparatively examined: discrete cosine transform, wavelet transform and a direct method. Classification performances, training times and the numbers of nodes of Kohonen network, Restricted Coulomb Energy (RCE) network and the hybrid neural network are presented. To increase the classification performance and to decrease the number of nodes, the hybrid neural network is trained by Genetic Algorithms (GAs). Ten types of ECG beats obtained from the MIT-BIH database and from a real-time ECG measurement system are classified with a success of 98% by using the hybrid neural network structure and discrete cosine transform together. [ABSTRACT FROM AUTHOR]

In this work, a new multi-objective optimization algorithm called multi-objective learner performance-based behavior algorithm is proposed. The proposed algorithm is based on the process of moving graduated students from high school to college. The proposed technique produces a set of non-dominated solutions. To test the ability and efficacy of the proposed multi-objective algorithm, it is applied to a group of benchmarks and five real-world engineering optimization problems. Several widely used metrics are employed in the quantitative statistical comparisons. The proposed algorithm is compared with three multi-objective algorithms: Multi-Objective Water Cycle Algorithm (MOWCA), Non-dominated Sorting Genetic Algorithm (NSGA-II), and Multi-Objective Dragonfly Algorithm (MODA). The produced results for the benchmarks and engineering problems show that in general the accuracy and diversity of the proposed algorithm are better compared to the MOWCA and MODA. However, the NSGA-II outperformed the proposed work in some of the cases and showed better accuracy and diversity. Nevertheless, in problems, such as coil compression spring design problem, the quality of solutions produced by the proposed algorithm outperformed all the participated algorithms. Moreover, in regard to the processing time, the proposed work provided better results compared with all the participated algorithms. [ABSTRACT FROM AUTHOR]

Parkinson's disease is one of the most prevalent neurodegenerative sicknesses distinguished by motor function impairment. Parkinson's disease (PD) diagnosis is a complicated job that demands the evaluation of numerous non-motor and motor signs. Throughout the analysis of vocal or speech abnormalities are notable indications that doctors should think. Early diagnosis of PD is essential for preliminary treatment and assisting the doctor to heal and evade the PD's spread in other brain cells and save several lives. So, this study introduces an adaptive expert diagnostic system to predict PD accurately. This suggested system proposes a hybrid methodology: two-stage mutual information and autoencoder-based dimensionality reduction approach with genetically optimized LightGBM (MI-AE-GOLGBM) algorithm, to improve the proposed system's performance and predict the best outcomes. The proposed MI-AE-GOLGBM approach comprises four methodologies: mutual information, autoencoder, genetic algorithm, and LightGBM algorithm, in which mutual information and autoencoder are implemented to form a two-stage dimensionality reduction approach for selecting the informative features from the input dataset and hence producing a reduced dataset with the most significant newly generated features, and genetic algorithm is employed to intelligently optimize the hyperparameters of LightGBM algorithm in which LightGBM algorithm utilizes such newly generated features and the best-optimized hyperparameters provided by the two-stage mutual information and autoencoder-based dimension reduction methods and the genetic algorithm, respectively, to which to classify the PD sufferers and healthy controls and enhance the precision value and reliability of the proposed system. Four different real-world publicly available Parkinson's disease datasets are employed in this proposed research to assess and verify the proposed methodology's performance. This proposed research utilizes different machine learning (ML) algorithms to compare our proposed approach's performance. The outcomes reveal that the proposed methodology can produce the best predictions based on voice data relating to the PD compared to the different ML algorithms. [ABSTRACT FROM AUTHOR]

Economic early warning is the recognition and judgment of the state of economic operation, and its research results directly affect the rational formulation of macro-control policies. However, the traditional early warning methods are mainly based on expert experience or simple statistical model, which are difficult to reflect the nature of highly nonlinear economic system and can not meet the objective requirements of macroeconomic early warning. Based on the above background, the purpose of this study is to design an economic early warning system based on improved genetic and BP hybrid algorithm and neural network. Based on the overview of macroeconomic early warning at home and abroad, this study expounds the design of early warning index system, early warning model, establishment of early warning system and other issues in the macroeconomic early warning theoretical system; deeply analyses the theoretical methods of BP neural network and adaptive mutation genetic algorithm, and discusses the feasibility of realizing macroeconomic early warning by BP neural network and adaptive mutation genetic algorithm, The improved genetic and BP hybrid algorithm and neural network economic early warning model are established. Finally, the experimental results show that the correlation coefficient between the composite index and the comprehensive early warning is 0.89, and the delay number is 0, which shows that the early warning index obtained by the early warning system can accurately reflect the actual economic fluctuations. The results show that the improved genetic and BP hybrid algorithm and neural network economic early warning system are effective, feasible and have good accuracy. [ABSTRACT FROM AUTHOR]

With the exponential growth of the amount of data being generated, stored and processed on a daily basis in the machine learning, data analytics and decision-making systems, the data preprocessing established itself as the key factor for building reliable high-performance machine learning models. One of the roles in preprocessing is variable reduction using feature selection methods; however, the processing time needed for these methods is a major drawback. This study aims at mitigating this problem by migrating the algorithm to a MapReduce implementation suitable for parallelization on a high number of commodity hardware units. The genetic algorithm-based methods were put in the focus of this study. Hadoop, an open-source MapReduce library, was used as a framework for implementing parallel genetic algorithms within our research. The representative machine learning methods, SVM (support vector machine), ANN (artificial neural network), RT (random tree), logistic regression and Naive Bayes, were embedded into implementation for feature selection. The feature selection methods were applied to four NSL-KDD data sets, and the number of features is reduced from cca 40 to cca 10 data sets with the accuracy of 90.45%. These results have both significant practical and theoretical impact. On the one hand, the genetic algorithm has been parallelized in the MapReduce manner, which has been considered unachievable in a strict sense. Furthermore, the genetic algorithm allows randomness-enhanced feature selection and its parallelization reduces overall data preprocessing and allows larger population count which in turn leads to better feature selection. On the practical side, it has been shown that this implementation outperforms the existing feature selection methods. [ABSTRACT FROM AUTHOR]

Clustering is a commonly used method for exploring and analysing data where the primary objective is to categorise observations into similar clusters. In recent decades, several algorithms and methods have been developed for analysing clustered data. We notice that most of these techniques deterministically define a cluster based on the value of the attributes, distance, and density of homogenous and single-featured datasets. However, these definitions are not successful in adding clear semantic meaning to the clusters produced. Evolutionary operators and statistical and multidisciplinary techniques may help in generating meaningful clusters. Based on this premise, we propose a new evolutionary clustering algorithm (ECA*) based on social class ranking and meta-heuristic algorithms for stochastically analysing heterogeneous and multifeatured datasets. The ECA* is integrated with recombinational evolutionary operators, Levy flight optimisation, and some statistical techniques, such as quartiles and percentiles, as well as the Euclidean distance of the K-means algorithm. Experiments are conducted to evaluate the ECA* against five conventional approaches: K-means (KM), K-means++ (KM++), expectation maximisation (EM), learning vector quantisation (LVQ), and the genetic algorithm for clustering++ (GENCLUST++). That the end, 32 heterogeneous and multifeatured datasets are used to examine their performance using internal and external and basic statistical performance clustering measures and to measure how their performance is sensitive to five features of these datasets (cluster overlap, the number of clusters, cluster dimensionality, the cluster structure, and the cluster shape) in the form of an operational framework. The results indicate that the ECA* surpasses its counterpart techniques in terms of the ability to find the right clusters. Significantly, compared to its counterpart techniques, the ECA* is less sensitive to the five properties of the datasets mentioned above. Thus, the order of overall performance of these algorithms, from best performing to worst performing, is the ECA*, EM, KM++, KM, LVQ, and the GENCLUST++. Meanwhile, the overall performance rank of the ECA* is 1.1 (where the rank of 1 represents the best performing algorithm and the rank of 6 refers to the worst performing algorithm) for 32 datasets based on the five dataset features mentioned above. [ABSTRACT FROM AUTHOR]

The backpropagation (BP) algorithm is a gradient-based algorithm used for training a feedforward neural network (FNN). Despite the fact that BP is still used today when FNNs are trained, it has some disadvantages, including the following: (i) it fails when non-differentiable functions are addressed, (ii) it can become trapped in local minima, and (iii) it has slow convergence. In order to solve some of these problems, metaheuristic algorithms have been used to train FNN. Although they have good exploration skills, they are not as good as gradient-based algorithms at exploitation tasks. The main contribution of this article lies in its application of novel memetic approaches based on the Gravitational Search Algorithm (GSA) and Chaotic Gravitational Search Algorithm (CGSA) algorithms, called respectively Memetic Gravitational Search Algorithm (MGSA) and Memetic Chaotic Gravitational Search Algorithm (MCGSA), to train FNNs in three classical benchmark problems: the XOR problem, the approximation of a continuous function, and classification tasks. The results show that both approaches constitute suitable alternatives for training FNNs, even improving on the performance of other state-of-the-art metaheuristic algorithms such as ParticleSwarm Optimization (PSO), the Genetic Algorithm (GA), the Adaptive Differential Evolution algorithm with Repaired crossover rate (Rcr-JADE), and the Covariance matrix learning and Bimodal distribution parameter setting Differential Evolution (COBIDE) algorithm. Swarm optimization, the genetic algorithm, the adaptive differential evolution algorithm with repaired crossover rate, and the covariance matrix learning and bimodal distribution parameter setting differential evolution algorithm. [ABSTRACT FROM AUTHOR]

The output effect of fractional-order stochastic resonance (FOSR) system is affected by many factors such as input system parameters and noise intensity. In practice, many tests are needed to adjust parameters to achieve the optimal effect, and this way of "trial and error" greatly limits the application prospect of FOSR. Based on genetic algorithm, a suitable adaptive function was established to adjust the multiple parameters, including the fractional order, system parameters, and the input noise intensity of the fractional bistable system. Simulation results showed that the algorithm can achieve joint optimization of these parameters. It was proved that this algorithm is conducive to the real-time adaptive adjustment of the FOSR system in practical applications and conducive to the application and extension of FOSR in weak signal detection and other fields. The proposed algorithm has certain practical value. [ABSTRACT FROM AUTHOR]

The idea of improving human's life quality by making life more comfortable and easy is nowadays possible using current technologies and techniques to solve complex daily problems. The presented idea in this work proposes a control strategy for autonomous robotic systems, specifically car-like robots. The main objective of this work is the development of a reactive navigation controller by means of obstacles avoidance and position control to reach a desired position in an unknown environment. This research goal was achieved by the integration of potential fields and neuroevolution controllers. The neuro-evolutionary controller was designed using the (NEAT) algorithm "Neuroevolution of Augmented Topologies" and trained using a designed training environment. The methodology used allowed the vehicle to reach a certain level of autonomy, obtaining a stable controller that includes kinematic and dynamic considerations. The obtained results showed significant improvements compared to the comparison work. [ABSTRACT FROM AUTHOR]

The goal of this work is to propose a hybrid algorithm called real-coded self-organizing migrating genetic algorithm by combining real-coded genetic algorithm (RCGA) and self-organizing migrating algorithm (SOMA) for solving bound-constrained nonlinear optimization problems having multimodal continuous functions. In RCGA, exponential ranking selection, whole-arithmetic crossover and non-uniform mutation operations have been used as different operators where as in SOMA, a modification has been done. The performance of the proposed hybrid algorithm has been tested by solving a set of benchmark optimization problems taken from the existing literature. Then, the simulated results have been compared numerically and graphically with existing algorithms. In the graphical comparison, a modified performance index has been proposed. Finally, the proposed algorithm has been applied to solve two real-life problems. [ABSTRACT FROM AUTHOR]

League championship algorithm is a recently proposed population-based evolutionary algorithm for finding global optimal solutions in continuous optimization problems. The proposed work adopts the algorithm by modifying the team formation step for solving real-time task scheduling problem in heterogeneous multiprocessors. Two different objectives: tardiness and energy consumption were considered for scheduling. Our proposed algorithm is implemented using Java and tested using the graphs generated by the benchmark tools: task graph for free and task graph generator. Simulation results prove the performance of the proposed algorithm is better in terms of the objective functions over the other existing metaheuristic algorithms such as genetic algorithm, ant colony optimization and particle swarm optimization. [ABSTRACT FROM AUTHOR]

A Quantiser Neural Network (QNN) is proposed for the segmentation of MR and CT images. Elements of a feature vector are formed by image intensities at one neighbourhood of the pixel of interest. QNN is a novel neural network structure, which is trained by genetic algorithms. Each node in the first layer of the QNN forms a hyperplane (HP) in the input space. There is a constraint on the HPs in a QNN. The HP is represented by only one parameter in d dimensional input space. Genetic algorithms are used to find the optimum values of the parameters which represent these nodes. The novel neural network is comparatively examined with a multilayer perceptron and a Kohonen network for the segmentation of MR and CT head images. It is observed that the QNN gives the best classification performance with fewer nodes after a short training time. [ABSTRACT FROM AUTHOR]