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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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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...
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Leveraging Digital Trace Data to Investigate and Support Human-Centered Work Processes
The ongoing digitization of processes in all domains of everyday life driven by IT systems shows great potential for process automation, analysis,...
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Dynamic Data-Flow Analysis with Dacite: Evaluating an Integrated Data-Flow Visualization Approach
According to different studies, analyzing the data-flow coverage when testing programs is a highly effective approach to ensure software quality....
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Impact Analysis of Disruptions on Composite Resources
This paper examines the impact of disruptions on the ongoing consumption of composite resources assigned to jobs. Composite resources are defined...
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Enhancing Software Defect Prediction: Exploring the Predictive Power of Two Data Flow Metrics
Data flow coverage criteria find extensive application in software testing, yet scant research exists regarding low-level data flow metrics as...
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Requirements Elicitation in the Age of AI: A Tool’s Multi-system Journey
Traditional Requirements Engineering (RE) practices have introduced new tools to elicit and model requirements. Applying these tools to building AI...
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A Bigraphs Paper of Sorts
Bigraphs are an expressive graphical modelling formalism to represent systems with a mix of both spatial and non-local connectivity. Currently it is...
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Generative AI for Code Generation: Software Reuse Implications
Generative AI has lately started being used in the software engineering process. Developers are relying on ChatGPT, GitHub Copilot or other tools to...
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Using Energy Consumption for Self-adaptation in FaaS
One of the programming models that has been develo** the most in recent years is Function as a Service (FaaS). The growing concern over data centre...
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On Modularity of Neural Networks: Systematic Review and Open Challenges
Modularity is used to manage the complexity of monolithic software systems and is a de facto practice in software engineering. Similar modularity...
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Using Code from ChatGPT: Finding Patterns in the Developers’ Interaction with ChatGPT
ChatGPT can advise developers and provide code on how to fix bugs, add new features, refactor, reuse, and secure their code but currently, there is...