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Memetic Computation

The Mainspring of Knowledge Transfer in a Data-Driven Optimization Era

  • Book
  • © 2019

Overview

Authors:
  1. Abhishek Gupta

    1. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

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  2. Yew-Soon Ong

    1. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

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    You can also search for this author in PubMed   Google Scholar

  • Presents a data-driven view of optimization through the framework of memetic computation (MC)
  • Provides the first comprehensive coverage of memetic computation
  • Includes a summary of the complete timeline of MC research activities
  • Explores newly emerging problem settings from the optimization literature in a theoretical manner and systematically describes the associated algorithmic developments that align with the general theme of memetics
  • Offers novel theories and algorithms for principled transfer and multitask optimization
  • Introduces the novel idea of meme-based search space compression for large-scale optimization

Part of the book series: Adaptation, Learning, and Optimization (ALO, volume 21)

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Table of contents (7 chapters)

  1. Front Matter

    Pages i-xi
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  2. Introduction: The Rise of Memetics in Computing

    • Abhishek Gupta, Yew-Soon Ong
    Pages 1-13

  3. Human Crafted Memes

    1. Front Matter

      Pages 15-15
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    2. Canonical Memetic Algorithms

      • Abhishek Gupta, Yew-Soon Ong
      Pages 17-26

    3. Data-Driven Adaptation in Memetic Algorithms

      • Abhishek Gupta, Yew-Soon Ong
      Pages 27-43

  4. Machine Crafting Memes

    1. Front Matter

      Pages 45-45
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    2. The Memetic Automaton

      • Abhishek Gupta, Yew-Soon Ong
      Pages 47-61

    3. Sequential Knowledge Transfer Across Problems

      • Abhishek Gupta, Yew-Soon Ong
      Pages 63-82

    4. Multitask Knowledge Transfer Across Problems

      • Abhishek Gupta, Yew-Soon Ong
      Pages 83-92

    5. Future Direction: Compressed Meme Space Evolutions

      • Abhishek Gupta, Yew-Soon Ong
      Pages 93-101

  5. Back Matter

    Pages 103-104
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Keywords

About this book

This book bridges the widening gap between two crucial constituents of computational intelligence: the rapidly advancing technologies of machine learning in the digital information age, and the relatively slow-moving field of general-purpose search and optimization algorithms. With this in mind, the book serves to offer a data-driven view of optimization, through the framework of memetic computation (MC). The authors provide a summary of the complete timeline of research activities in MC – beginning with the initiation of memes as local search heuristics hybridized with evolutionary algorithms, to their modern interpretation as computationally encoded building blocks of problem-solving knowledge that can be learned from one task and adaptively transmitted to another. In the light of recent research advances, the authors emphasize the further development of MC as a simultaneous problem learning and optimization paradigm with the potential to showcase human-like problem-solvingprowess; that is, by equipping optimization engines to acquire increasing levels of intelligence over time through embedded memes learned independently or via interactions. In other words, the adaptive utilization of available knowledge memes makes it possible for optimization engines to tailor custom search behaviors on the fly – thereby paving the way to general-purpose problem-solving ability (or artificial general intelligence). In this regard, the book explores some of the latest concepts from the optimization literature, including, the sequential transfer of knowledge across problems, multitasking, and large-scale (high dimensional) search, systematically discussing associated algorithmic developments that align with the general theme of memetics.
 
The presented ideas are intended to be accessible to a wide audience of scientific researchers, engineers, students, and optimization practitioners who are familiar with the commonly used terminologies of evolutionary computation. A full appreciation of the mathematical formalizations and algorithmic contributions requires an elementary background in probability, statistics, and the concepts of machine learning. A prior knowledge of surrogate-assisted/Bayesian optimization techniques is useful, but not essential.



Authors and Affiliations

  • School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Abhishek Gupta, Yew-Soon Ong

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