rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Evolutionary Approach to Machine Learning and Deep Neural Networks
    sub title: Neuro-Evolution and Gene Regulatory Networks
  name:
    name part: Iba, Hitoshi.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: si
    date issued: 2018
    edition: 1st ed. 2018.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XIII, 245 p. 127 illus., 84 illus. in color. online resource.
  abstract: 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.
  table of contents: Introduction -- Meta-heuristics, machine learning and deep learning methods -- Evolutionary approach to deep learning -- Machine learning approach to evolutionary computation -- Evolutionary approach to gene regulatory networks -- Conclusion.
  note: by Hitoshi Iba.
  subject:
    topic: Artificial intelligence
  subject:
    topic: Bioinformatics
  subject:
    topic: Biomathematics
  subject:
    topic: Computational intelligence
  subject:
    topic: Artificial Intelligence
  subject:
    topic: Bioinformatics
  subject:
    topic: Mathematical and Computational Biology
  subject:
    topic: Computational Intelligence
  classification: Q334-342
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  identifier: 9789811302008
  identifier: https://doi.org/10.1007/978-981-13-0200-8
  record info:
    record creation date: 180615
    record change date: 20191028231759.0
    record identifier: 978-981-13-0200-8