What Is Neuroevolution
Neuroevolution, sometimes spelled neuro-evolution, is a form of artificial intelligence that generates artificial neural networks (ANN), parameters, and rules through the application of evolutionary algorithms. Neuroevolution is also spelled neuro-evolution. The most popular applications for this technique are found in evolutionary robotics, artificial life, and general game playing. The primary advantage is that neuroevolution may be applied to a wider variety of problems than supervised learning methods, which need a curriculum of accurate input-output pairings to function properly. Neuroevolution, on the other hand, needs nothing more than a measurement of how well a network performs at a given job. For instance, the result of a game can be easily measured even if the necessary strategies are not provided in the form of labeled examples. Neuroevolution is frequently utilized as a component of the reinforcement learning paradigm. It can be contrasted with conventional deep learning approaches, which make use of gradient descent on a neural network that possesses a fixed topology. Neuroevolution is frequently utilized as a component of the reinforcement learning paradigm.
How You Will Benefit
(I) Insights, and validations about the following topics:
Chapter 1: Neuroevolution
Chapter 2: Artificial neural network
Chapter 3: Evolutionary algorithm
Chapter 4: Genetic representation
Chapter 5: Effective fitness
Chapter 6: Neuroevolution of augmenting topologies
Chapter 7: Recurrent neural network
Chapter 8: Compositional pattern-producing network
Chapter 9: HyperNEAT
Chapter 10: Evolving intelligent system
(II) Answering the public top questions about neuroevolution.
(III) Real world examples for the usage of neuroevolution in many fields.
Who This Book Is For
Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of neuroevolution.
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