Automatic design of machine learning via evolutionary computation: A survey

Grammar-based evolutionary approach for automated workflow composition with domain-specific operators and ensemble diversity

“…As mentioned earlier, EvoFlow is a G3P-based technique for AWC that, unlike most existing approaches in the literature [52], does not enforce a prefixed workflow structure. More precisely, compared to current grammar-based approaches, it does not restrict the type of preprocessing algorithms to be applied or their order within the workflow sequence.…”

Evolutionary Computation: Theories, Techniques, and Applications

“…There are also many open-source libraries and toolkits available for evolutionary computation in a variety of programming languages [32][33][34][35][36][37][38][39][40][41], making the application of evolutionary algorithms to new problems and domains particularly easy. Evolutionary computation has been effective in solving problems with a variety of characteristics, and within many application domains, such as multiobjective optimization [42][43][44][45], data science [46], machine learning [47][48][49], classification [50], feature selection [51], neural architecture search [52], neuroevolution [53], bioinformatics [54], scheduling [55], algorithm selection [56], computer vision [57], hardware validation [58], software engineering [59,60], and multi-task optimization [61,62], among many others.…”

Real-Time Monitoring Method for Thyristor Losses in Ultra High Voltage Converter Station Based on Wavelet Optimized GA-BP Neural Network

“…But its calculation accuracy is not high and some of the parameters required for the calculation are difficult to be obtained in the different types of thyristors. In recent years, data-driven modeling methods based on massive data have developed rapidly [18][19][20][21], and artificial intelligence algorithms are widely used in electrical engineering fields such as power equipment fault diagnosis, load forecasting, power system optimization, etc. [22][23][24].…”