3 results
Now showing1 - 3 of 3
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Enhancing surrogate model usability for optimisation experts through extended ML support in EvoAl(Association for Computing Machinery (ACM), 2026-08-13); ; ; Surrogate models are a crucial aspect in many real-world application optimisation problems. Thus, optimisation experts do not only have to focus on the optimisation problem at hand, but also work on selecting and configuring appropriate models. This may be challenging, when no extensive expertise in machine learning (ML) is present. To address this issue, we extend the open-source data science research tool EvoAl to better support optimisation practitioners in focusing on their core tasks. Specifically, we enhance EvoAl’s machine learning language to accommodate the growing complexity of surrogate modelling workflows. Our contributions include expanding the range of available models, introducing runtime validation of model configurations, and enabling the integration of pre-trained models via ONNX. These improvements lower the barrier to applying advanced ML techniques in optimisation, thereby facilitating more efficient and robust surrogate-based optimisation in practical applications.Publicationtype: Conference Paper5 3 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Repetitive Processes and Their Surrogate-Model Congruent Encoding for Evolutionary Algorithms - A Theoretic Proposal(2023-07); ; Evolutionary algorithms are a well-known optimisation technique. They can handle very different optimisation tasks and deal with distorted search spaces as well as non-differentiable optimisation functions. One crucial aspect in the design of evolutionary algorithms is the choice of encoding. Especially its interplay with the other components of the evolutionary algorithm is a relevant factor for the success of an evolutionary algorithm. While some encoding situations are relatively trivial, others pose a challenge. We focus on encoding repetitive processes, i.e. processes that consist of several variations of the same basic process (only with varied parameters). Our work proposes a possible technique that enables the validity of the encoded search space. We also provide adaptions to the standard operators of evolutionary algorithms to ensure they produce valid solutions. Furthermore, we show how this encoding technique is compatible with using a surrogate function for the fitness calculation and may reduce the necessary training data.Publicationtype: Conference Paper12 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Adapting mutation and recombination operators to range-aware relations in real-world application data(2022-07); ; Optimisation problems with higher-dimensional search spaces do usually not only come with equality or inequality constraints, but also with dependencies between the different variables. In real-world applications, especially in experimental data from material sciences, these relations as well as the constraints may not be true for the entire search space, but only for certain areas. Other constraints or relations may hold then for different areas of the search space. We build on correlation-aware mutation and recombination operators that are used in genetic algorithms and adjust them to be able to deal with area-specific constraints and relations. This can be configured by a domain expert using a domain-specific language. Our approach is evaluated with well-known benchmark functions, carefully designed distributions, and data description files and shows a better capability of generating feasible solutions in the population.Publicationtype: Conference Paper29