This book is a collection of the most recent approaches that combine metaheuristics and machine learning. Some of the methods considered in this book are evolutionary, swarm, machine learning, and deep learning. The chapters were classified based on the content; then, the sections are thematic. Different applications and implementations are included; in this sense, the book provides theory and practical content with novel machine learning and metaheuristic algorithms.
The chapters were compiled using a scientific perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics and is useful in courses on Artificial Intelligence, Advanced Machine Learning, among others. Likewise, the book is useful for research from the evolutionary computation, artificial intelligence, and image processing communities.
Cross Entropy Based Thresholding Segmentation of Magnetic Resonance Prostatic Images Using Metaheuristic Algorithms.- Hyperparameter Optimization in a Convolutional Neural Network Using Metaheuristic Algorithms.- Diagnosis of collateral effects in climate change through the identification of leaf damage using a novel heuristics and machine learning framework.- Feature engineering for Machine Learning and Deep Learning assisted Wireless Communication.- Genetic operators and their impact on the training of deep neural networks.- Implementation of metaheuristics with Extreme Learning Machines.- Architecture optimization of convolutional neural networks by micro genetic algorithms.- Optimising Connection Weights in Neural Networks using a Memetic Algorithm Incorporating Chaos Theory.- A review of metaheuristic optimization algorithms for wireless sensor networks.- A Metaheuristic Algorithm for Classification of White Blood Cells in Healthcare Informatics.- A Review of multi-lƒ$
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