Titlebook: Evolutionary Approach to Machine Learning and Deep Neural Networks; Neuro-Evolution and Hitoshi Iba Book 2018 Springer Nature Singapore Pt

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https://doi.org/10.1007/BFb0097558gging, boosting, Gr?bner bases, relevance vector machine, affinity propagation, SVM, and .-nearest neighbors. These are applied to the extension of GP (Genetic Programming), DE (Differential Evolution), and PSO (Particle Swarm Optimization).

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Espaces vectoriels topologiquesrks. Gene regulatory networks express the interactions between genes in an organism. We first give several inference methods to GRN. Then, we explain the real-world application of GRN to robot motion learning. We show how GRNs have generated effective motions to specific humanoid tasks. Thereafter,

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https://doi.org/10.1007/978-981-13-0200-8Evolutionary Computation; Evolutionary Computation; Meta-Heuristics; Deep Learning; Machine Learning; Gen

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Meta-heuristics, Machine Learning, and Deep Learning Methods,This chapter introduces several meta-heuristics and learning methods, which will be employed in later chapters. These methods will be employed to extend evolutionary computation frameworks in later chapters. Readers familiar with these methods may skip this chapter.

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Book 2018eneration of deep neural networks, and also describes how evolutionary methods are extended in combination with machine learning techniques. Chapter 4 includes novel methods such as particle swarm optimization based on affinity propagation (PSOAP), and transfer learning for differential evolution (T

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Evolutionary Approach to Machine Learning and Deep Neural NetworksNeuro-Evolution and