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From Genetic Variation to Probabilistic Modeling
Genetic algorithms ⦓GAs) [53, 83] are stochastic optimization methods inspired by natural evolution and genetics. Over the last few decades, GAs have...
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Hierarchical Bayesian Optimization Algorithm
The previous chapter has discussed how hierarchy can be used to reduce problem complexity in black-box optimization. Additionally, the chapter has...
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The Challenge of Hierarchical Difficulty
Thus far, we have examined the Bayesian optimization algorithm (BOA), empirical results of its application to several problems of bounded difficulty,...
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Hierarchical BOA in the Real World
The last chapter designed hBOA, which was shown to provide scalable solution for hierarchical traps. Since hierarchical traps were designed to test...
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Bayesian Optimization Algorithm
The previous chapter argued that using probabilistic models with multivariate interactions is a powerful approach to solving problems of bounded...
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Probabilistic Model-Building Genetic Algorithms
The previous chapter showed that variation operators in genetic and evolutionary algorithms can be replaced by learning a probabilistic model of...
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Scalability Analysis
The empirical results of the last chapter were tantalizing. Easy and hard problems were automatically solved without user intervention in polynomial...
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Summary and Conclusions
The purpose of this chapter is to provide a summary of main contributions of this work and outline important conclusions.
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Application of neural networks to predict indoor air temperature in a building with artificial ventilation: impact of early stop**
Indoor air temperature prediction can facilitate energy-saving actions without compromising the indoor thermal comfort of occupants. The aim of this...
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VLSI realization of hybrid fast fourier transform using reconfigurable booth multiplier
A discrete fourier transform (DFT) of a series of samples may be quickly and efficiently computed with the use of a mathematical procedure known as...
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Analyzing processing time and load factor: 5-node mix network with ElGamal encryption and XOR shuffling
To provide anonymous communication, this paper proposes the implementation of a 5-node mix network using ElGamal encryption and XOR Shuffling. An...
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Improving predictive performance in e-learning through hybrid 2-tier feature selection and hyper parameter-optimized 3-tier ensemble modeling
The paper presents a new feature selection technique developed in detail here to address improved prediction accuracy not only for the...
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Optimal feature with modified bi-directional long short-term memory for big data classification in healthcare application
Artificial intelligence together with its applications are advancing in all fields, particularly medical science. A considerable quantity of clinical...
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Software verification challenges in the blockchain ecosystem
Blockchain technology has created a new software development context, with its own peculiarities, mainly due to the guarantees that the technology...
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Intelligent Personality Assessment and Verification from Handwriting using Machine Learning
It is possible to tell a lot about a person just by looking at their handwriting. The way someone writes might tell you a lot about who, they are as...
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Systematizing modeler experience (MX) in model-driven engineering success stories
Modeling is often associated with complex and heavy tooling, leading to a negative perception among practitioners. However, alternative paradigms,...
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Federated learning for digital healthcare: concepts, applications, frameworks, and challenges
Various hospitals have adopted digital technologies in the healthcare sector for various healthcare-related applications. Due to the effect of the...
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Understanding the GDPR from a requirements engineering perspective—a systematic map** study on regulatory data protection requirements
Data protection compliance is critical from a requirements engineering (RE) perspective, both from a software development lifecycle (SDLC)...
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Why the use of domain-specific modeling in airworthy software requires new methods and how these might look like? (extended version)
The use of domain-specific modeling (DSM) in safety-critical avionics is rare, even though the ever-increasing complexity of avionics systems makes...
