evolutionary algorithms (EAs) have been found successful in the solution of a wide variety of optimization problems. However, EAs are unconstrained search techniques. Thus, incorporating constraints into the fitness f...
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ISBN:
(纸本)9781450392686
evolutionary algorithms (EAs) have been found successful in the solution of a wide variety of optimization problems. However, EAs are unconstrained search techniques. Thus, incorporating constraints into the fitness function of an EA is an open research area.
The research examines the optimization of fleet management of a shipping company through control algorithms, as finding an algorithm that will reduce a marine company’s exposure to risk by diversifying its fleet comp...
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Soil and tire interaction is a complex process that involves the exchange of variable stresses along the contact area of soil and tire. Despite this complexity, the description of this process in the form of mathemati...
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Soil and tire interaction is a complex process that involves the exchange of variable stresses along the contact area of soil and tire. Despite this complexity, the description of this process in the form of mathematical models has long been of interest to the researchers. The same complexity has led the wheels and soil interaction patterns to be constantly evolving and optimizing. This evolution has coincided with the scientific progress of mathematics, modeling and computer until today. Nowadays, optimizing and predicting a model based on input variables using machine learning techniques and conventional evolutionary algorithms play an important role in predicting the relationships between input and output. These methods can be far better than the conventional statistical techniques. The modeling and prediction of wheel rolling resistance on the soil have many parameters. Using new techniques such as genetic, BAT and PSO algorithms to optimize them seems to be suitable approaches. The aim of this research is to investigate and optimize the parameters of the Wismer-Luth model using the evolutionary algorithms. To improve the model, the variables of multi-pass, forward velocity and depth of the cone index, are also incorporated to the Wismer-Luth model, and the corresponding parameters are optimized with the BAT algorithm. Analysis of experimental data showed that the correlation of the output of the proposed model with the experimental data is 0.87 where it is 0.77 for the Wismer-Luth model. Furthermore, experimental results in this study showed that there is a significant relationship between rolling resistance and multi-pass effect, neglected in most models.
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.
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.
Recent developments in Generative Deep Learning have fostered new engineering methods for protein design. Although deep generative models trained on protein sequence can learn biologically meaningful representations, ...
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ISBN:
(数字)9781665467087
ISBN:
(纸本)9781665467087
Recent developments in Generative Deep Learning have fostered new engineering methods for protein design. Although deep generative models trained on protein sequence can learn biologically meaningful representations, the design of proteins with optimised properties remains a challenge. We combined deep learning architectures with evolutionary computation to steer the protein generative process towards specific sets of properties to address this problem. The latent space of a Variational Autoencoder is explored by evolutionary algorithms to find the best candidates. A set of single-objective and multi-objective problems were conceived to evaluate the algorithms' capacity to optimise proteins. The optimisation tasks consider the average proteins' hydrophobicity, their solubility and the probability of being generated by a defined functional Hidden Markov Model profile. The results show that evolutionary algorithms can achieve good results while allowing for more variability in the design of the experiment, thus resulting in a much greater set of possibly functional novel proteins.
Landslides, the most significant geohazards in Iran, adversely affect the region's socioeconomic conditions and the environment. Landslide susceptibility mapping is crucial for proactive risk management, sustainab...
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Landslides, the most significant geohazards in Iran, adversely affect the region's socioeconomic conditions and the environment. Landslide susceptibility mapping is crucial for proactive risk management, sustainable development, and protecting human lives, infrastructure, and the environment. It enables decision-makers to make informed choices, implement appropriate mitigation measures, and plan for potential landslide events, leading to safer and more resilient communities. Using a recommended subdivision methodology, this research has developed a landslide susceptibility map for a significant Gilan, Iran region. The production of the map involved utilizing an artificial neural network (ANN) model. This model incorporated sixteen causal components from several characteristics, including topographic and geomorphologic features, geological factors, land use patterns, hydrological aspects, and hydrogeological properties. Three hundred seventy instances were identified using multiple verified sources and analyzing aerial photographs to create the landslide inventory map. Examining and verifying the weights assigned to the causal elements were conducted per accepted mathematical standards, incorporating sensitivity analysis, earlier research, and empirical data on landslides. The area under the receiver operating characteristic curve (AUROC) was used to compare the various models. The study's findings indicated that the best swarm size value for COA-MLP1 is equal to 450, and the accuracy indices for this algorithm were 0.998 and 0.995 in training and testing datasets, respectively. Similarly, the AUC2 for the HSMLP,3 SFS-MLP,4 and TLBO-MLP5 were 0.997, 0.999, and 0.999 in training and 0.995, 0.996, and 0.995 in the testing dataset, respectively. Therefore, the use of optimization algorithms leads to an increase in the performance and accuracy of the neural network, and the high accuracy of the SFS-MLP model demonstrates the existence of a dependable criterion for deline
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.
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.
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