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.
-
A Context-Aware Framework to Support Decision-Making in Production Planning
In the scope of Industry 4.0, this paper showcases the design and implementation of a context-aware decision-making framework that simulates the...
-
Implementation Patterns for Zone Architectures in Enterprise-Grade Data Lakes
In industry practice, zone models have been established as data lake architectures of choice to enable the reuse of data preparation, data modeling,...
-
Designing Military Command and Control Systems as System of Systems – An Analysis of Stakeholder Needs and Challenges
In the context of capability development and to respond to the influence of new technology, a conceptual framework to support the integration of new...
-
Variants of Variants: Context-Based Variant Analysis for Process Mining
An essential aspect of analyzing processes with process mining is the notion of variants. Analysts can make better decisions and improve processes by...
-
On the Flexibility of Declarative Process Specifications
Declarative process specifications, such as Declare, provide a natural framework to capture flexible business processes. However, the specification...
-
Improving Simplicity by Discovering Nested Groups in Declarative Models
Discovering simple, understandable and yet accurate process models is a well-known issue for models mined from real-life event logs. In this paper,...
-
Improving Requirement Traceability by Leveraging Video Game Simulations in Search-Based Software Engineering
Video games pose different challenges during development and maintenance than classic software. For example, common and widespread assets, that are...
-
Towards a Comprehensive Evaluation of Decision Rules and Decision Mining Algorithms Beyond Accuracy
Decision mining algorithms discover decision points and the corresponding decision rules in business processes. So far, the evaluation of decision...
-
Observability for Quantum Workflows in Heterogeneous Multi-cloud Environments
Quantum workflows enable a robust, scalable, and reliable orchestration of hybrid applications comprising classical and quantum tasks. Varying...
-
A Graph Language Modeling Framework for the Ontological Enrichment of Conceptual Models
Conceptual models (CMs) offer a structured way to organize and communicate information in information systems. However, current models lack adequate...
-
Low-Modeling of Software Systems
There is a growing need for better development methods and tools to keep up with the increasing complexity of new software systems. New types of user...