Evolutionary Approach to Machine Learning and Deep Neural Networks

Author: Hitoshi Iba
Publisher: Springer
ISBN: 9789811302008
Release Date: 2018-06-15
Genre: Computers

This book provides theoretical and practical knowledge about a methodology for evolutionary algorithm-based search strategy with the integration of several machine learning and deep learning techniques. These include convolutional neural networks, Gröbner bases, relevance vector machines, transfer learning, bagging and boosting methods, clustering techniques (affinity propagation), and belief networks, among others. The development of such tools contributes to better optimizing methodologies. Beginning with the essentials of evolutionary algorithms and covering interdisciplinary research topics, the contents of this book are valuable for different classes of readers: novice, intermediate, and also expert readers from related fields. Following the chapters on introduction and basic methods, Chapter 3 details a new research direction, i.e., neuro-evolution, an evolutionary method for the generation 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 (TRADE), another machine learning approach for extending differential evolution. The last chapter is dedicated to the state of the art in gene regulatory network (GRN) research as one of the most interesting and active research fields. The author describes an evolving reaction network, which expands the neuro-evolution methodology to produce a type of genetic network suitable for biochemical systems and has succeeded in designing genetic circuits in synthetic biology. The author also presents real-world GRN application to several artificial intelligent tasks, proposing a framework of motion generation by GRNs (MONGERN), which evolves GRNs to operate a real humanoid robot.

Advanced Methodologies and Technologies in Artificial Intelligence Computer Simulation and Human Computer Interaction

Author: Khosrow-Pour, D.B.A., Mehdi
Publisher: IGI Global
ISBN: 9781522573692
Release Date: 2018-09-28
Genre: Computers

As modern technologies continue to develop and evolve, the ability of users to adapt with new systems becomes a paramount concern. Research into new ways for humans to make use of advanced computers and other such technologies through artificial intelligence and computer simulation is necessary to fully realize the potential of tools in the 21st century. Advanced Methodologies and Technologies in Artificial Intelligence, Computer Simulation, and Human-Computer Interaction provides emerging research in advanced trends in robotics, AI, simulation, and human-computer interaction. Readers will learn about the positive applications of artificial intelligence and human-computer interaction in various disciples such as business and medicine. This book is a valuable resource for IT professionals, researchers, computer scientists, and researchers invested in assistive technologies, artificial intelligence, robotics, and computer simulation.

Analysis of Biological Networks

Author: Björn H. Junker
Publisher: John Wiley & Sons
ISBN: 9781118209912
Release Date: 2011-09-20
Genre: Computers

An introduction to biological networks and methods for their analysis Analysis of Biological Networks is the first book of its kind to provide readers with a comprehensive introduction to the structural analysis of biological networks at the interface of biology and computer science. The book begins with a brief overview of biological networks and graph theory/graph algorithms and goes on to explore: global network properties, network centralities, network motifs, network clustering, Petri nets, signal transduction and gene regulation networks, protein interaction networks, metabolic networks, phylogenetic networks, ecological networks, and correlation networks. Analysis of Biological Networks is a self-contained introduction to this important research topic, assumes no expert knowledge in computer science or biology, and is accessible to professionals and students alike. Each chapter concludes with a summary of main points and with exercises for readers to test their understanding of the material presented. Additionally, an FTP site with links to author-provided data for the book is available for deeper study. This book is suitable as a resource for researchers in computer science, biology, bioinformatics, advanced biochemistry, and the life sciences, and also serves as an ideal reference text for graduate-level courses in bioinformatics and biological research.

Bioinformatics Algorithms

Author: Ion Mandoiu
Publisher: John Wiley & Sons
ISBN: 0470253428
Release Date: 2008-02-15
Genre: Computers

Presents algorithmic techniques for solving problems in bioinformatics, including applications that shed new light on molecular biology This book introduces algorithmic techniques in bioinformatics, emphasizing their application to solving novel problems in post-genomic molecular biology. Beginning with a thought-provoking discussion on the role of algorithms in twenty-first-century bioinformatics education, Bioinformatics Algorithms covers: General algorithmic techniques, including dynamic programming, graph-theoretical methods, hidden Markov models, the fast Fourier transform, seeding, and approximation algorithms Algorithms and tools for genome and sequence analysis, including formal and approximate models for gene clusters, advanced algorithms for non-overlapping local alignments and genome tilings, multiplex PCR primer set selection, and sequence/network motif finding Microarray design and analysis, including algorithms for microarray physical design, missing value imputation, and meta-analysis of gene expression data Algorithmic issues arising in the analysis of genetic variation across human population, including computational inference of haplotypes from genotype data and disease association search in case/control epidemiologic studies Algorithmic approaches in structural and systems biology, including topological and structural classification in biochemistry, and prediction of protein-protein and domain-domain interactions Each chapter begins with a self-contained introduction to a computational problem; continues with a brief review of the existing literature on the subject and an in-depth description of recent algorithmic and methodological developments; and concludes with a brief experimental study and a discussion of open research challenges. This clear and approachable presentation makes the book appropriate for researchers, practitioners, and graduate students alike.

Recent developments in biologically inspired computing

Author: Leandro N. De Castro
Publisher: IGI Global
ISBN: UOM:39015060104885
Release Date: 2005
Genre: Computers

Recent Developments in Biologically Inspired Computing is necessary reading for undergraduate and graduate students, and researchers interested in knowing the most recent advances in problem solving techniques inspired by nature. This book covers the most relevant areas in computational intelligence, including evolutionary algorithms, artificial neural networks, artificial immune systems and swarm systems. It also brings together novel and philosophical trends in the exciting fields of artificial life and robotics. This book has the advantage of covering a large number of computational approaches, presenting the state-of-the-art before entering into the details of specific extensions and new developments. Pseudocodes, flow charts and examples of applications are provided so as to help newcomers and mature researchers to get the point of the new approaches presented.

Statistical and Machine Learning Approaches for Network Analysis

Author: Matthias Dehmer
Publisher: John Wiley & Sons
ISBN: 9781118346983
Release Date: 2012-06-26
Genre: Mathematics

Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks. Comprised of chapters written by internationally renowned researchers in the field of interdisciplinary network theory, the book presents current and classical methods to analyze networks statistically. Methods from machine learning, data mining, and information theory are strongly emphasized throughout. Real data sets are used to showcase the discussed methods and topics, which include: A survey of computational approaches to reconstruct and partition biological networks An introduction to complex networks—measures, statistical properties, and models Modeling for evolving biological networks The structure of an evolving random bipartite graph Density-based enumeration in structured data Hyponym extraction employing a weighted graph kernel Statistical and Machine Learning Approaches for Network Analysis is an excellent supplemental text for graduate-level, cross-disciplinary courses in applied discrete mathematics, bioinformatics, pattern recognition, and computer science. The book is also a valuable reference for researchers and practitioners in the fields of applied discrete mathematics, machine learning, data mining, and biostatistics.

Encyclopedia of Artificial Intelligence

Author:
Publisher: IGI Global Snippet
ISBN: 9781599048499
Release Date: 2009
Genre: Artificial intelligence

"This book is a comprehensive and in-depth reference to the most recent developments in the field covering theoretical developments, techniques, technologies, among others"--Provided by publisher.

Evolution after Gene Duplication

Author: Katharina Dittmar
Publisher: John Wiley & Sons
ISBN: 9781118148099
Release Date: 2011-06-09
Genre: Science

Gene duplication has long been believed to have played a major role in the rise of biological novelty through evolution of new function and gene expression patterns. The first book to examine gene duplication across all levels of biological organization, Evolution after Gene Duplication presents a comprehensive picture of the mechanistic process by which gene duplication may have played a role in generating biodiversity. Key Features: Explores comparative genomics, genome evolution studies and analysis of multi-gene families such as Hox, globins, olfactory receptors and MHC (immune system) A complete post-genome treatment of the topic originally covered by Ohno's 1970 classic, this volume extends coverage to include the fate of associated regulatory pathways Taps the significant increase in multi-gene family data that has resulted from comparative genomics Comprehensive coverage that includes opposing theoretical viewpoints, comparative genomics data, theoretical and empirical evidence and the role of bioinformatics in the study of gene duplication This up-to-date overview of theory and mathematical models along with practical examples is suitable for scientists across various levels of biology as well as instructors and graduate students.