Titlebook: Automated Design of Machine Learning and Search Algorithms; Nelishia Pillay,Rong Qu Book 2021 Springer Nature Switzerland AG 2021 Machine

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Automated Design of Classification Algorithms,ing network intrusion detection and financial forecasting. The automated design required less design time and produced classifiers that performed better than the manually designed classifiers. Grammatical evolution was found to produce better-performing classifiers for binary classification and gene

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Characteristics of Mold Materialsvely inject a bias in the design process and consequently reduce its variance. As confirmed by the empirical studies realized so far, this reduces the risk of overfitting simulation models and improves the chances of crossing the reality gap successfully.

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https://doi.org/10.1007/978-3-662-28676-0ing Salesman Problem and Multidimensional Knapsack Problem). Empirical results indicate that the approach can generate both constructive and perturbative heuristics that outperform well-known heuristic methods in a number of cases and are competitive with specialised methods for some instances.

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Flat Glass as a Construction Material,lgorithms directly operating on the solution space. In that respect, by design, a hyper-heuristic can be applied to any problem with a search space of quantifiable solutions. This flexibility coming from their easy-to-use nature has been validated in various academic and real-world applications. The

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