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Preliminary

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Optinformatics in Evolutionary Learning and Optimization

Part of the book series: Adaptation, Learning, and Optimization ((ALO,volume 25))

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Abstract

Evolutionary algorithm is one of the most well-established classes of meta-heuristics. Besides optinformatics in evolutionary learning and optimization, knowledge mining and reuse has also been proposed for improving the performance of many meta-heuristic methods. To provide the reader a comprehensive background of optinformatics, this chapter presents a review on meta-heuristics and knowledge learning and transfer in meta-heuristics. Moreover, as optinformatics represents a form of memetic computation which has been defined as a computational paradigm that incorporates the notion of meme(s) as basic units of transferable information encoded in computational representations for enhancing the performance of artificial evolutionary systems in the domain of search and optimization [1], the introduction of memetic computation is also presented in this chapter.

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Author information

Authors and Affiliations

  1. College of Computer Science, Chongqing University, Chongqing, China

    Liang Feng

  2. School of Computer Science and Technology, Dalian University of Technology, Dalian, China

    Yaqing Hou

  3. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

    Zexuan Zhu

Authors
  1. Liang Feng
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  2. Yaqing Hou
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  3. Zexuan Zhu
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Correspondence to Liang Feng .

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Feng, L., Hou, Y., Zhu, Z. (2021). Preliminary. In: Optinformatics in Evolutionary Learning and Optimization. Adaptation, Learning, and Optimization, vol 25. Springer, Cham. https://doi.org/10.1007/978-3-030-70920-4_2

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