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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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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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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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Eigenvalues II, Matrix Diagonalization and Unitary Matrix
In this chapter, we will show another method to find the eigenvalues of a matrix. We further show that the process of finding the eigenvalues of a...
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Pauli Spin Matrices, Adjoint Matrix, and Hermitian Matrix
In this chapter, I will show you some of the tedious mathematical derivations step-by-step. They are not difficult, and you just need to be patient...
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The Last But Not the Least
In this chapter, we will discuss how to write scripts on IBM-Q to run more sophisticated quantum programs. At this final step, you will find that you...
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No-Cloning Theorem and Quantum Teleportation I
In this chapter, we first prove the no-cloning theorem. We will show that we cannot clone or copy an arbitrary quantum state. Then we will introduce...
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Drivers, Barriers, and Enablers of Digital Transformation in Maritime Ports Sector: A Review and Aggregate Conceptual Analysis
This paper develops a conceptual framework for digital transformation in the maritime ports sector. The study combines a systematic literature review...
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Network Robustness Improvement Based on Alternative Paths Consideration
Many transportation networks have complex infrastructures (road, rail, airspace, etc.). The quality of service in air transportation depends on...
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Federated Learning for Drowsiness Detection in Connected Vehicles
Ensuring driver readiness poses challenges, yet driver monitoring systems can assist in determining the driver’s state. By observing visual cues,...
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Performance Analysis of Compiler Support for Parallel Evaluation of C++ Constant Expressions
Metaprogramming, the practice of writing programs that manipulate other programs at compile-time, continues to impact software development; enabling...
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IoT Attacks Countermeasures: Systematic Review and Future Research Direction
In order to connect heterogeneous nodes, objects, and smart devices of a network, such as e-transportation, e-health, e-education, e-home, and...
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Exception Handling in Real-Time and Embedded Systems
Modern computing not only demands precise execution but also timely and responsive actions. Nowhere is this truer than in the realm of real-time and...
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An Auditable Framework for Evidence Sharing and Management Using Smart Lockers and Distributed Technologies: Law Enforcement Use Case
This paper presents a decentralised framework for sharing and managing evidence that uses smart lockers, blockchain technology, and the...