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Optinformatics Within a Single Problem Domain

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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

This chapter introduces specific algorithm designs of optinformatics in evolutionary learning and optimization within a single problem domain. In particular, the first algorithm considers the knowledge learning and reuse in the form of local search which is integrated in a global search, such as genetic algorithm, with the aim of improving the search efficiency and effectiveness of global optimization. The well-known feature selection is used as the practical problem to evaluate the performance of the built optinformatics method. Further, towards general knowledge learning and transfer in evolutionary search, a paradigm that integrates evolutionary search with transfer learning is presented. Taking vehicle routing and arc routing problem as the case study, knowledge is defined beyond the form of local search. Based on the knowledge definition, the learning, selection, variation, and imitation of knowledge in evolutionary search for routing are discussed. To validate the performance of the optinformatic algorithm, comprehensive empirical studies on commonly used vehicle routing and arc routing problems are conducted.

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Notes

  1. 1.

    A meme is defined as the basic unit of cultural transmission in [63] stored in brains. In the context of computational intelligence, memes are defined as recurring real-world patterns or knowledge encoded in computational representations for the purpose of effective problem-solving [64].

  2. 2.

    If a database of knowledge memes that are learned from relevant past problem solving experiences in the same domain is available, it can be loaded and leveraged upon.

  3. 3.

    Note that as the learning operation is conducted offline, it does not incur additional cost to the evolutionary optimization of \(\mathbf {p}^j_{new}\).

  4. 4.

    Dependency is a measure of the correlation of two data sets [68]. Here the interest on knowledge meme \(\mathbf {M}\) in the form of a maximization of the statistical dependency thus ensure maximal alignment between the transformed tasks distribution and the tasks distribution of the optimized solution.

  5. 5.

    From the experimental study, the problems in the benchmark set are mostly verified to be positively correlated.

  6. 6.

    Maximum mean discrepancy measures the distribution differences between two data sets, which can come in the form of vectors, sequences, graphs, and other common structured data types.

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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

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  1. Liang Feng
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Correspondence to Liang Feng .

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Feng, L., Hou, Y., Zhu, Z. (2021). Optinformatics Within a Single Problem Domain. 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_3

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