The theory of evolutionary computation for discrete search spaces has made significant progress since the early 2010s. This survey summarizes some of the most important recent results in this research area. It discuss...
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To protect the security of IT systems of companies and organizations, Role Based Access Control is a widely used concept. The corresponding optimization problem, the Role Mining Problem, which consists of finding an o...
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Asynchronous evolutionary algorithms are becoming increasingly popular as a means of making full use of many processors while solving computationally expensive search and optimization problems. These algorithms excel ...
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Asynchronous evolutionary algorithms are becoming increasingly popular as a means of making full use of many processors while solving computationally expensive search and optimization problems. These algorithms excel at keeping large clusters fully utilized, but may sometimes inefficiently sample an excess of fast-evaluating solutions at the expense of higher-quality, slow-evaluating ones. We have previously introduced a steady-state parent selection strategy, SWEET ("Selection whilE EvaluaTing"), that sometimes selects individuals that are still being evaluated and allows them to reproduce early. We perform a takeover-time analysis that confirms that this strategy gives slow-evaluating individuals that have higher fitnesses an increased ability to multiply in the population. We also find that SWEET appears effective at improving optimization performance on problems in which solution quality is positively correlated with evaluation time. We evaluate our approach on six simulated real-valued optimization problems and three real-world applications: an autonomous vehicle controller problem that involves tuning a spiking neural network and two adversarial EA problems. We further evaluate SWEET versus a basic asynchronous process in a simulated setting. We present evidence that SWEET outperforms basic asynchronous processes in a use-case in which performance is positively correlated with evaluation time, and performs comparably (and often better) than basic asynchronous processes in several use-cases where performance is negatively correlated with evaluation time. That said, in the cases where performance and evaluation time are negatively correlated the variance of outcomes for SWEET is notably high.
An automated sizing approach for analog circuits using evolutionary algorithms is presented in this paper. A targeted search of the search space has been implemented using a particle generation function and a repair-b...
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An automated sizing approach for analog circuits using evolutionary algorithms is presented in this paper. A targeted search of the search space has been implemented using a particle generation function and a repair-bounds function that has resulted in faster convergence to the optimal solution. The algorithms are tuned and modified to converge to a better optimal solution with less standard deviation for multiple runs compared to standard versions. Modified versions of the artificial bee colony optimisation algorithm, genetic algorithm, grey wolf optimisation algorithm, and particle swarm optimisation algorithm are tested and compared for the optimal sizing of two operational amplifier topologies. An extensive performance evaluation of all the modified algorithms showed that the modifications have resulted in consistent performance with improved convergence for all the algorithms. The implementation of parallel computation in the algorithms has reduced run time. Among the considered algorithms, the modified artificial bee colony optimisation algorithm gave the most optimal solution with consistent results across multiple runs.
In light of the rapidly growing number of photovoltaic micro-grids, the modelling of their short-term power yields based on meteorological measurements is increasing in significance. This requires the knowledge of tot...
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In light of the rapidly growing number of photovoltaic micro-grids, the modelling of their short-term power yields based on meteorological measurements is increasing in significance. This requires the knowledge of total and diffuse instantaneous solar radiation;however, most meteorological stations conduct actinometric measurements only with regard to total solar radiation, especially on a minute scale. This paper contains an analysis of the currently used PV cell mathematical model and suggests its modification aimed at calculating PV cell power with satisfactory accuracy, without the knowledge of diffuse solar radiation. Three function families were proposed to approximate the relationship between diffuse irradiance and the total and theoretical total irradiance variance for a cloudless sky. A program has been implemented to identify functions from the aforementioned function families. It leverages an evolution strategy algorithm and a fitness function based on the least-squares point method. It was employed to calculate the desired functions based on actual measurement data. The outcome was the sought-after dependence that enables predicting diffuse irradiance based on more frequently available measurement data.
作者：
Altares-Lopez, SergioGarcia-Ripoll, Juan JoseRibeiro, AngelaCSIC
Ctr Automat & Robot CAR CSIC UPM Ctra Campo Real Km 0200 Arganda Del Rey 28500 Spain CSIC
Inst Fis Fundamental IFF Consejo Super Invest Cient Calle Serrano 113b Madrid 28006 Spain Univ Politecn Madrid
Programa Doctorado Automat & Robot Calle Jose Gutierrez Abascal 2 Madrid 28006 Spain
A new hybrid system is proposed for automatically generating and training quantum-inspired classifiers on grayscale images by using multiobjective genetic algorithms. It is defined a dynamic fitness function to obtain...
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A new hybrid system is proposed for automatically generating and training quantum-inspired classifiers on grayscale images by using multiobjective genetic algorithms. It is defined a dynamic fitness function to obtain the smallest circuit complexity and highest accuracy on unseen data, ensuring that the proposed technique is generalizable and robust. At the same time, it is minimized the complexity of the generated circuits in terms of the number of entangling operators by penalizing their appearance and number of gates. The size of the images is reduced by using two dimensionality reduction approaches: principal component analysis (PCA), which is encoded within the individual and genetically optimized by the system, and a small convolutional autoencoder (CAE). These two methods are compared with one another and with a classical nonlinear approach to understand their behaviors and to ensure that the classification ability is due to the quantum circuit and not the preprocessing technique used for dimensionality reduction.
Having a full situational awareness while driving is one of the most important perceptions for safe driving which can be reduced by various factors such as in-vehicle infotainment, distraction, or mental load leading....
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Having a full situational awareness while driving is one of the most important perceptions for safe driving which can be reduced by various factors such as in-vehicle infotainment, distraction, or mental load leading. Machine learning methods are being used to optimize for the identification of these inhibiting factors. To do so, three types of data were used: biographic features, physiological signals and vehicle information of 68 participants are being utilized to identify the normal and loaded behaviors. This research, therefore, concentrates on driving behavior analysis using a new automated hybrid framework for detection of performance degradation of drivers due to distraction. The proposed model contains a hybrid of extreme learning neural network, as an ensemble learning method and evolutionary algorithms, to determine the weights of classifiers, for combining several traditional classifiers. The obtained results showcase that the proposed model yields outstanding performance than the other applied methods.
Color variations in H&E histological images can impact the segmentation and classification stages of computational systems used for cancer diagnosis. To address these variations, normalization techniques can be ap...
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Color variations in H&E histological images can impact the segmentation and classification stages of computational systems used for cancer diagnosis. To address these variations, normalization techniques can be applied to adjust the colors of histological images. Estimates of stain color appearance matrices and stain density maps can be employed to carry out these color adjustments. This study explores these estimates by leveraging a significant biological characteristic of stain mixtures, which is represented by a sparsity parameter. Computationally estimating this parameter can be accomplished through various sparsity measures and evolutionary algorithms. Therefore, this study aimed to evaluate the effectiveness of different sparsity measures and algorithms for color normalization of H&E-stained histological images. The results obtained demonstrated that the choice of different sparsity measures significantly impacts the outcomes of normalization. The sparsity metric l(epsilon)(0) proved to be the most suitable for it. Conversely, the evolutionary algorithms showed little variations in the conducted quantitative analyses. Regarding the selection of the best evolutionary algorithm, the results indicated that particle swarm optimization with a population size of 250 individuals is the most appropriate choice.
The last few years have seen an increasing demand for high-capacity Internet services, and this need has intensified in the years 2020 and 2021. In 2020 and 2021, Internet usage grew by 50% in several European countri...
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The last few years have seen an increasing demand for high-capacity Internet services, and this need has intensified in the years 2020 and 2021. In 2020 and 2021, Internet usage grew by 50% in several European countries, mainly due to home office, video streaming, hybrid teaching, and others. High-capacity optical networks usually meet this growing demand for Internet services. Thus, investigations that can improve the quality of optical networks are highly relevant in the current context. One of the research problems in this area is related to the physical topology design (PTD) of optical networks, which is classified as NP-hard. Several studies on PTD consider the application of meta-heuristics that obtain suboptimal solutions in a time compatible with engineering applications. However, meta-heuristics and local search techniques have been combined in several other optimization problems, which is not typical for the PTD problem. This paper proposes a solution to the PTD problem that combines a known multipurpose optimization algorithm, the NSGA-III, with operators considering the domain-specific knowledge of the problem to provide superior-quality networks. According to our results, the new proposal presents quality up to 8% higher than previous proposals concerning the hypervolume metrics (HV), maintaining a similar computational cost.
An enhanced gain-scheduling control strategy was presented to establish a smooth adaptation mechanism for the longitudinal motion of B747 between different flight envelopes. The focal objective is to accomplish a smoo...
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An enhanced gain-scheduling control strategy was presented to establish a smooth adaptation mechanism for the longitudinal motion of B747 between different flight envelopes. The focal objective is to accomplish a smooth and non-conservative gain-scheduling control system that has high performance and stability characteristics for different flight conditions and transitions between them. The proposed approach ensured a successful gain scheduling control system by allocating a specific error performance index that should be minimized for all flight conditions. Then all required performance indices were cohered according to the flight envelope in the form of a multi-objective optimization problem that could be solved using evolutionary algorithms. The performance and stability of the transition points between different flight conditions were preserved through the sets of feasible solutions obtained by an evolutionary algorithm which are known as a set of Pareto optimal solutions. The proposed gain-scheduling system was built based on 2DoF-PID (Two-degree-of-freedom Proportional Integral Derivative) controller. An auto-decision-maker was developed to adjust the parameters of the 2DoF-PID gainscheduling control system with the current flight condition. The effectiveness of the proposed gain-scheduling control structure was studied for two different multiobjective evolutionary-based optimizers known as fast sorting and elite multi-objective genetic *** (***), and sub-population genetic *** (SPGA. II). The proposed methodology was evaluated by simulation results according to the quality of the pareto-front and transient response characteristics of the proposed gain-scheduling controller for the chosen flight conditions in normal flight conditions and 50% loss of actuator effectiveness.
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