Electroencephalography (EEG) has emerged as a primary non-invasive and mobile modality for understanding the complex workings of the human brain, providing invaluable insights into cognitive processes, neurological di...
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Electroencephalography (EEG) has emerged as a primary non-invasive and mobile modality for understanding the complex workings of the human brain, providing invaluable insights into cognitive processes, neurological disorders, and brain-computer interfaces. Nevertheless, the volume of EEG data, the presence of artifacts, the selection of optimal channels, and the need for feature extraction from EEG data present considerable challenges in achieving meaningful and distinguishing outcomes for machine learning algorithms utilized to process EEG data. Consequently, the demand for sophisticated optimization techniques has become imperative to overcome these hurdles effectively. evolutionary algorithms (EAs) and other nature-inspired metaheuristics have been applied as powerful design and optimization tools in recent years, showcasing their significance in addressing various design and optimization problems relevant to brain EEG-based applications. This paper presents a comprehensive survey highlighting the importance of EAs and other metaheuristics in EEG-based applications. The survey is organized according to the main areas where EAs have been applied, namely artifact mitigation, channel selection, feature extraction, feature selection, and signal classification. Finally, the current challenges and future aspects of EAs in the context of EEG-based applications are discussed.
Program synthesis techniques offer the potential to allow non-programmers to create computer programs. Approaches based on evolutionary algorithms are known for their performance in program synthesis. Since 2015, the ...
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ISBN:
(纸本)9781450392686
Program synthesis techniques offer the potential to allow non-programmers to create computer programs. Approaches based on evolutionary algorithms are known for their performance in program synthesis. Since 2015, the general program synthesis benchmark suite offers a common pool of benchmark problems, which makes different approaches comparable. To analyze what has been achieved so far, we identified in a recent literature review the main evolutionary program synthesis approaches, determined the difficulty of the common benchmark problems, and discussed current challenges. In this short paper, we present the difficulty ranking of the benchmark problems and summarize the current challenges in program synthesis with evolutionary algorithms.
The interest in understanding nanofluid density's impact on heat transfer and fluid flow behaviors has driven the need for accurate density values. Artificial intelligence techniques for predicting nanofluid densi...
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The interest in understanding nanofluid density's impact on heat transfer and fluid flow behaviors has driven the need for accurate density values. Artificial intelligence techniques for predicting nanofluid density provide a cost-effective and efficient alternative to labor-intensive lab experiments. In the current research, four distinct models based on the Radial Basis Function (RBF) Neural Network were developed and implemented on an extensive and comprehensive databank comprising 4004 experimental data-points gathered from multiple available sources. Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Imperialist Competitive Algorithm (ICA), and Genetic Algorithm (GA) were used separately to optimize the neural network. The provided databank introduces 95 varieties of mono-nanofluids, including 16 types of nano-particles and 11 types of base- fluids. The target/dependent variable in this research is the density of mono-nanofluids (rho nf), whereas the input/ independent variables include the average nano-particle diameter (dnp), nano-particle mass concentration (phi m), temperature (T), pressure (P), nano-particle density (rho np), and base-fluid density (rho bf). Various analyses of the four models confirmed the RBF-ACO model's robustness and superiority. Key statistical indicators for this model indicated an Average Absolute Percent Relative Error (AAPRE) of 0.5391%, a Standard Deviation (SD) of 0.0099, and a Coefficient of Determination (R2) of 0.9818. Sensitivity analysis for the superior model identified key input-output variables with high relevancy factors (r-values). Notably, variables phi m and rho bf had maximum rvalues close to 0.70, indicating their significant role in predicting mono-nanofluids' density. In addition, a leverage statistical approach was utilized to determine possible outliers and the applicability domain of the RBFACO model.
This paper proposes a practical method to diminish the computational complexity of the controllers using predictions based on the evolutionary Algorithm (EA). It is the case of Receding Horizon Control structures whos...
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ISBN:
(数字)9781665467469
ISBN:
(纸本)9781665467469
This paper proposes a practical method to diminish the computational complexity of the controllers using predictions based on the evolutionary Algorithm (EA). It is the case of Receding Horizon Control structures whose Controller can integrate an EA to generate the optimal predictions. In a previous paper, the authors proposed the control range adaptation to diminish the computational complexity. An additional technique called control range tuning is proposed in this paper.
In a free market, the creation of hospitals, schools, sports and public residential facilities, requires the expertise-and possibly the capital-of the private sector. The traditional contract, in which the public admi...
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ISBN:
(纸本)9783031024627;9783031024610
In a free market, the creation of hospitals, schools, sports and public residential facilities, requires the expertise-and possibly the capital-of the private sector. The traditional contract, in which the public administration pays private operators to make or maintain buildings and services, is flanked by public private partnership, in which the private operator is usually delegated to carry out the entire process receiving a fixed fee. For years, governments and administrations have been incentivized to use this kind of contract, assuming that it would increase the building qualities and reduce the risk of higher expenses. Empirical evidence refutes this assumption, and this can be caused by to the so-called moral hazard of the private operator. One of the main problem in public private partnership is the difficulty to define an optimal risk allocation, as there no formulas exist to simulate the performance of the contract in advance. In this paper, evolutionary algorithms are used to compute an optimal specifications document, while, at the same time, foreseeing an optimal effort in work. Experimental results clearly demonstrate the feasibility of this approach, also helping the public administration to check if their knowledge is sufficient to structure an efficient specifications document.
Population size is an important variable in evolutionary algorithms (EA). Its proper configuration improves the performance of the search process not only in terms of the fitness function but also for the resources re...
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ISBN:
(数字)9781665467087
ISBN:
(纸本)9781665467087
Population size is an important variable in evolutionary algorithms (EA). Its proper configuration improves the performance of the search process not only in terms of the fitness function but also for the resources required. This article introduced a population management mechanism that includes different operators. Such operators are designed and applied based on the diversity of the population. In general terms, the operators address problems in EA regarding stagnation and the inefficient use of the function evaluations. As a case of study, the proposed method is applied in the Differential Evolution (DE) to provide it the ability to change its population size according to its needs. The experimental results and comparisons demonstrate greatly improved performance when compared to the unmodified DE, some of its most successful variants, and other much more complex algorithms from the state-of-the-art.
Due to the growing importance of electric vehicles, charging stations (CS) deployment is becoming an important issue in many cities. The aim of this paper is to introduce a novel evolutionary-based approach for solvin...
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Due to the growing importance of electric vehicles, charging stations (CS) deployment is becoming an important issue in many cities. The aim of this paper is to introduce a novel evolutionary-based approach for solving the CS deployment problem. This study investigates many aspects of the formulation of this approach, such as the design variables selection and the definition of a feasibility function, to improve both effectiveness and flexibility. In particular, the latter is a key factor compared to many other state-of-the-art approaches: in fact, it can be used with most of the available evolutionary algorithms (EAs) and can manage different quality-of-service performance parameters. The proposed approach is successfully compared with a greedy optimization on the case study of the City of Milan (Italy) using four different EAs. Two different performance parameters have been defined and used to prove the flexibility of the proposed approach. The results show its very good convergence rate and the quality of the obtained solutions.
As the world shifts towards a low-carbon economy, the strategic deployment of renewable energy sources (RESs) is critical for maximizing energy output and ensuring sustainability. This study introduces GREENIA, a nove...
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As the world shifts towards a low-carbon economy, the strategic deployment of renewable energy sources (RESs) is critical for maximizing energy output and ensuring sustainability. This study introduces GREENIA, a novel artificial intelligence (AI)-powered framework for optimizing RES placement that holistically integrates machine learning (gated recurrent unit neural networks with swish activation functions and attention layers), evolutionary optimization algorithms (Jaya), and Shapley additive explanations (SHAPs). A key innovation of GREENIA is its ability to provide natural language explanations (NLEs), enabling transparent and interpretable insights for both technical and non-technical stakeholders. Applied in Greece, the framework addresses the challenges posed by the interplay of meteorological factors from 10 different meteorological stations across the country. Validation against real-world data demonstrates improved prediction accuracy using metrics like root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). SHAP analysis enhances transparency by identifying key meteorological influences, such as temperature and humidity, while NLE translates these insights into actionable recommendations in natural language, improving accessibility for energy planners and policymakers. The resulting strategic plan offers precise, intelligent, and interpretable recommendations for deploying RES technologies, ensuring maximum efficiency and sustainability. This approach not only advances renewable energy optimization but also equips stakeholders with practical tools for guiding the strategic deployment of RES across diverse regions, contributing to sustainable energy management.
evolutionary algorithms (EAs) are powerful heuristic search approaches which relies on Darwinian evolution that capture global solutions to complex optimization problems which has powerful features of reliability and ...
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ISBN:
(纸本)9781665495813
evolutionary algorithms (EAs) are powerful heuristic search approaches which relies on Darwinian evolution that capture global solutions to complex optimization problems which has powerful features of reliability and versatility. (EAs) such as Particle swarm optimization (PSO) is a global optimization method that is extremely effective. PSO's flaws include slow convergence, premature convergence, and getting stuck at local optima. In this paper, chaotic map and dynamic-weight Particle Swarm Optimization (CHDPSOA) are combined with PSO to enhance the search strategy through adjusting the inertia weight of PSO and changing the position update formula in the (CHDPSOA), resulting in efficient balancing for local and global PSO feature selection processes. The performance of CHDPSOA was compared to that of three metaheuristic techniques: Differential Evolution (DE) and the original PSO, using eight numerical functions. The validation of this technique is carried out on four different datasets. The results show that the CHDPSOA is a good feature selection technique that balances the exploration and exploitation search processes to produce good results. The proposed CHDPSOA method performed well in correctly categorizing features using the KNN Classifier for all four datasets.
Chance constrained optimization problems allow to model problems where constraints involving stochastic components should only be violated with a small probability. evolutionary algorithms have been applied to this sc...
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ISBN:
(纸本)9781956792003
Chance constrained optimization problems allow to model problems where constraints involving stochastic components should only be violated with a small probability. evolutionary algorithms have been applied to this scenario and shown to achieve high quality results. With this paper, we contribute to the theoretical understanding of evolutionary algorithms for chance constrained optimization. We study the scenario of stochastic components that are independent and Normally distributed. Considering the simple single-objective (1+1) EA, we show that imposing an additional uniform constraint already leads to local optima for very restricted scenarios and an exponential optimization time. We therefore introduce a multi-objective formulation of the problem which trades off the expected cost and its variance. We show that multi-objective evolutionary algorithms are highly effective when using this formulation and obtain a set of solutions that contains an optimal solution for any possible confidence level imposed on the constraint. Furthermore, we prove that this approach can also be used to compute a set of optimal solutions for the chance constrained minimum spanning tree problem. Experimental investigations on instances of the NP-hard stochastic minimum weight dominating set problem confirm the benefit of the multi-objective approach in practice.
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