Titlebook: Evolutionary Machine Learning Techniques; Algorithms and Appli Seyedali Mirjalili,Hossam Faris,Ibrahim Aljarah Book 2020 Springer Nature Si

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https://doi.org/10.1007/978-981-32-9990-0Artificial Neural Network; Probabilistic Neural Network; Self-Optimizing Neural Network; Feedforward Ne

從容

https://doi.org/10.1007/978-3-031-24315-8 are discussed to show where AI optimization algorithms and machine learning techniques fit in. Different types of learning are briefly covered as well including supervised, unsupervised, and reinforcement techniques. The last part of this chapter includes discussions on evolutionary machine learning, which is the focus of this book.

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Introduction to Evolutionary Machine Learning Techniques, are discussed to show where AI optimization algorithms and machine learning techniques fit in. Different types of learning are briefly covered as well including supervised, unsupervised, and reinforcement techniques. The last part of this chapter includes discussions on evolutionary machine learning, which is the focus of this book.

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2524-7565 niques.Covers the application of improved artificial neural .This book provides an in-depth analysis of the current evolutionary machine learning techniques. Discussing the most highly regarded methods for classification, clustering, regression, and prediction, it includes techniques such as support

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Statins

Religiosity in the Films of Ingmar Bergman and optimizing the wear loss in carbon steel and was evaluated and tested using different performance criteria to ensure its reliability. The generated model can be utilized to monitor wear in mechanical components without requiring any human efforts to enhance the monitoring efficiency and reduce human errors.

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Salp Chain-Based Optimization of?Support Vector Machines and Feature Weighting for Medical Diagnostis (SVMs) simultaneously. A new and powerful metaheuristic called salp swarm algorithm is combined with SVM for this task. The designed SSA-SVM approach shows several merits compared to other SVM-based frameworks with well-regarded algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO).

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Efficient Moth-Flame-Based Neuroevolution Modelshe results are compared to well-known methods such as particle swarm optimizer (PSO), population-based incremental learning (PBIL), differential evolution (DE), and genetic algorithm (GA). The obtained results indicate the efficacy of the MFO-embedded neuroevolution model as a potential method in dealing with classification cases.

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Evolving Genetic Programming Models for Predicting Quantities of Adhesive Wear in Low and Medium Car and optimizing the wear loss in carbon steel and was evaluated and tested using different performance criteria to ensure its reliability. The generated model can be utilized to monitor wear in mechanical components without requiring any human efforts to enhance the monitoring efficiency and reduce human errors.