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Evolutionary approach to machine learning and deep neural networks neuro-evolution and gene regulatory networks /

Main Author: Iba, Hitoshi,
Other Authors: SpringerLink (Online service)
Format: eBook
Language: English
Published: Singapore : Springer, 2018.
Physical Description: 1 online resource.
Table of Contents:
  • Intro; Preface; References; Acknowledgements; Contents; 1 Introduction; 1.1 Evolution at Work; 1.2 Have We Solved the Problem of the Evolution of the Eye, Which Troubled Darwin?; 1.3 Evolutionary Algorithms: From Bullet Trains to Finance and Robots; 1.4 Genetic Programming and Its Genome Representation; 1.4.1 Tree-Based Representation of Genetic Programming; 1.4.2 Linear Genetic Programming; 1.5 Cartesian Genetic Programming (CGP)sym]CGP; 1.6 Interactive Evolutionary Computation (IEC); 1.7 Why Evolutionary Computation?; References.
  • 2 Meta-heuristics, Machine Learning, and Deep Learning Methods2.1 Meta-heuristics Methodologies; 2.1.1 PSO: Particle Swarm Optimization; 2.1.2 DE: Differential Evolution; 2.2 Machine Learning Techniques; 2.2.1 k-Means Algorithm; 2.2.2 SVM; 2.2.3 RVM: Relevance Vector Machine; 2.2.4 k-Nearest Neighbor Classifier; 2.2.5 Transfer Learning; 2.2.6 Bagging and Boosting; 2.2.7 Gröbner Bases; 2.2.8 Affinity Propagation and Clustering Techniques; 2.3 Deep Learning Frameworks; 2.3.1 CNN and Feature Extraction; 2.3.2 Generative Adversary Networks (GANsym]GAN) and Generating Fooling Images.
  • 2.3.3 Bayesian Networks and Loopy Belief PropagationReferences; 3 Evolutionary Approach to Deep Learning; 3.1 Neuroevolution; 3.1.1 NEAT and HyperNEAT; 3.1.2 CPPN and Pattern Generation; 3.2 Deep Neural Networks with Evolutionary Optimization; 3.2.1 Genetic Convolutional Neural Networks (Genetic CNNs); 3.2.2 Hierarchical Feature Construction Using GP; 3.2.3 Differentiable Pattern-Producing Network; References; 4 Machine Learning Approach to Evolutionary Computation; 4.1 BagGP and BoostGP; 4.2 Vanishing Ideal GP: Algebraic Approach to GP; 4.2.1 Symbolic Regression and GP.
  • 4.2.2 Vanishing Ideal4.2.3 VIGP: Reduction Process; 4.2.4 VIGP Versus GP Comparison; 4.2.5 VIGP for Rational Polynomials; 4.2.6 VIGP for the 6-Parity Problem; 4.3 The Kaizen Programming; 4.4 RVM-GP: RVM for Automatic Feature Selection in GP; 4.4.1 The Sequential Sparse Bayesian Learning Algorithm; 4.4.2 Model Selection in RVM-GP; 4.4.3 RVM-GP Performance; 4.5 PSOAP: Particle Swarm Optimization Based on Affinity Propagation; 4.6 Machine Learning for Differential Evolution; 4.6.1 ILSDE; 4.6.2 SVC-DE; 4.6.3 TRADE: TRAnsfer Learning for DE; 4.6.4 NENDE: k-NN Classifier for DE; References.
  • 5 Evolutionary Approach to Gene Regulatory Networks5.1 Overview of Gene Regulatory Networks; 5.2 GRN Inference by Evolutionary and Deep Learning Methods; 5.2.1 Inferring Genetic Networks; 5.2.2 INTERNe: IEC-Based GRN Inference with ERNe; 5.3 MONGERN: GRN Application for Humanoid Robots; 5.3.1 Evolutionary Robotics and GRN; 5.3.2 How to Express Motions; 5.3.3 How to Learn Motions; 5.3.4 How to Make Robust Motions; 5.3.5 Simulation Experiments with MONGERN; 5.3.6 Real Robot Experiments with MONGERN; 5.3.7 Robustness with MONGERN; 5.4 ERNe: A Framework for Evolving Reaction Networks.