Search
Search Results
-
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...
-
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...
-
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,...
-
-
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...
-
Bayesian Optimization Algorithm
The previous chapter argued that using probabilistic models with multivariate interactions is a powerful approach to solving problems of bounded...
-
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...
-
Scalability Analysis
The empirical results of the last chapter were tantalizing. Easy and hard problems were automatically solved without user intervention in polynomial...
-
Summary and Conclusions
The purpose of this chapter is to provide a summary of main contributions of this work and outline important conclusions.
-
An empirical study on cross-component dependent changes: A case study on the components of OpenStack
Modern software systems are composed of several loosely coupled components. Typical examples of such systems are plugin-based systems, microservices,...
-
Research on satellite link allocation algorithm for Earth-Moon space information network
With ongoing advancements in space exploration and communication technology, the realization of an Earth-Moon space information network is gradually...
-
An exploratory evaluation of code smell agglomerations
Code smell is a symptom of decisions about the system design or code that may degrade its modularity. For example, they may indicate inheritance...
-
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,...
-
Machine learning-driven performance assessment of network-on-chip architectures
System-on-chip designs for high-performance computing systems widely use network-on-chip (NoC) technology. The critical metrics such as latency,...
-
A new integrated steganography scheme for quantum color images
In this paper, we propose a quantum steganography scheme with a color image as the cover image. In order to enhance the security of the embedded...
-
Exploring recent advances in random grid visual cryptography algorithms
Visual cryptography scheme is initiated to securely encode a secret image into multiple shares. The secret can be reconstructed by overlaying the...
-
Cost-aware workflow offloading in edge-cloud computing using a genetic algorithm
The edge-cloud computing continuum effectively uses fog and cloud servers to meet the quality of service (QoS) requirements of tasks when edge...
-
An adaptive service deployment algorithm for cloud-edge collaborative system based on speedup weights
In the contemporary landscape of edge computing, the deployment of services with stringent real-time requirements on edge devices is increasingly...
-
iDOCEM
In the business process lifecycle, models can be approached from two perspectives: on the one hand, models are used to create systems in the design...
-
Exploring the potential of Wav2vec 2.0 for speech emotion recognition using classifier combination and attention-based feature fusion
AbstractSelf-supervised learning models, such as Wav2vec 2.0, extract efficient features for speech processing applications including speech emotion...
