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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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SymboleoPC: checking properties of legal contracts
Legal contracts specify requirements for business transactions. Symboleo was recently proposed as a formal specification language for legal...
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Guest editorial to the special section on PoEM’2022
This guest editorial presents the papers contributing to the 15th IFIP WG 8.1 Working Conference on the Practice of Enterprise Modelling (PoEM 2022)....
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Automated generation of smart contract code from legal contract specifications with Symboleo2SC
Smart contracts (SCs) are software systems that monitor and partially control the execution of legal contracts to ensure compliance with the...
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Human factors in model-driven engineering: future research goals and initiatives for MDE
Software modelling and model-driven engineering (MDE) is traditionally studied from a technical perspective. However, one of the core motivations...
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GSGP-hardware: instantaneous symbolic regression with an FPGA implementation of geometric semantic genetic programming
Geometric Semantic Genetic Programming (GSGP) proposed an important enhancement to GP-based learning, incorporating search operators that operate...
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Using reactive links to propagate changes across engineering models
Collaborative model-driven development is a de facto practice to create software-intensive systems in several domains (e.g., aerospace, automotive,...
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MBFair: a model-based verification methodology for detecting violations of individual fairness
Decision-making systems are prone to discrimination against individuals with regard to protected characteristics such as gender and ethnicity....
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ModelXGlue: a benchmarking framework for ML tools in MDE
The integration of machine learning (ML) into model-driven engineering (MDE) holds the potential to enhance the efficiency of modelers and elevate...
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OIL: an industrial case study in language engineering with Spoofax
Domain-specific languages (DSLs) promise to improve the software engineering process, e.g., by reducing software development and maintenance effort...
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Geometric semantic GP with linear scaling: Darwinian versus Lamarckian evolution
Geometric Semantic Genetic Programming (GSGP) has shown notable success in symbolic regression with the introduction of Linear Scaling (LS). This...
