Evolutionary approach to machine learning and deep neural networks : neuro-evolution and gene regulatory networks /: neuro-evolution and gene regulatory networks. (2018)
- Record Type:
- Book
- Title:
- Evolutionary approach to machine learning and deep neural networks : neuro-evolution and gene regulatory networks /: neuro-evolution and gene regulatory networks. (2018)
- Main Title:
- Evolutionary approach to machine learning and deep neural networks : neuro-evolution and gene regulatory networks
- Further Information:
- Note: Hitoshi Iba.
- Authors:
- Iba, Hitoshi
- 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 DifferentiableIntro; 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. … (more)
- Publisher Details:
- Singapore : Springer
- Publication Date:
- 2018
- Extent:
- 1 online resource
- Subjects:
- 006.3/1
Computer science
Machine learning
Neural networks (Computer science)
Evolutionary computation
Artificial intelligence
Bioinformatics
Engineering
COMPUTERS -- General
Evolutionary computation
Machine learning
Neural networks (Computer science)
Science -- Life Sciences -- Anatomy & Physiology
Mathematics -- Applied
Computers -- Intelligence (AI) & Semantics
Molecular biology
Maths for scientists
Artificial intelligence
Computer Science
Artificial Intelligence (incl. Robotics)
Bioinformatics
Mathematical and Computational Biology
Computational Intelligence
Electronic books - Languages:
- English
- ISBNs:
- 9789811302008
9811302006 - Related ISBNs:
- 9789811301995
9811301999 - Notes:
- Note: Includes bibliographical references and indexes.
Note: Online resource; title from PDF title page (SpringerLink, viewed June 19, 2018). - Access Rights:
- Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
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- Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.
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- Physical Locations:
- British Library HMNTS - ELD.DS.341313
- Ingest File:
- 03_015.xml