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Multi-modal Valley-Adaptive Memetic Algorithm for Efficient Discovery of First-Order Saddle Points

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Simulated Evolution and Learning (SEAL 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7673))

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Abstract

First-order saddle point represents an important landmark on the problem landscape. This point lies along the minimum energy path connecting two minima, more specifically at the point with maximum energy on the path. Unlike minima or maxima, to identify first-order saddle points require both maximization and minimization tasks. Finding such points is extremely difficult. In this paper, we present a real-coded memetic algorithm for locating first-order saddle points. The proposed algorithm leverage the advantage of valley- adaptive clearing scheme in maintaining multiple solutions and Schlegel algorithm in achieving fast and precise convergence. Empirical results shown that the proposed algorithms achieve more than 90% with converge speed of more than 100 fold when comparing to its evolutionary compeers.

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Author information

Authors and Affiliations

  1. Center of Computational Intelligence, School of Computer Engineering, Nanyang Technological University, Singapore

    Mostafa Ellabaan, Xianshun Chen & Nguyen Quang Huy

Authors
  1. Mostafa Ellabaan
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  2. Xianshun Chen
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  3. Nguyen Quang Huy
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Editor information

Editors and Affiliations

  1. Faculty of Information Technology, Le Quy Don Technical University, 100 Hoang Quoc Viet Street, Cau Giay District,, Hanoi, Vietnam

    Lam Thu Bui

  2. School of Computer Engineering, Nanyang Technological University, Block N4, 2b-39, Nanyang Avenue, 639798,, Singapore, Singapore

    Yew Soon Ong

  3. Faculty of Information Technology,, Hanoi University, km9 Nguyen Trai Road, Hanoi, Vietnam

    Nguyen Xuan Hoai

  4. Graduate School of Engineering, Department of Computer Science, Osaka Prefecture University, 1-1 Gakuen-cho, Nakaku, 599-8531, Sakai, Osaka, Japan

    Hisao Ishibuchi

  5. School of Electrical and Electronic Engineering, Nanyang Technological University, Block N4, 2b-39, Nanyang Avenue, 639798, Singapore, Singapore

    Ponnuthurai Nagaratnam Suganthan

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© 2012 Springer-Verlag Berlin Heidelberg

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Ellabaan, M., Chen, X., Huy, N.Q. (2012). Multi-modal Valley-Adaptive Memetic Algorithm for Efficient Discovery of First-Order Saddle Points. In: Bui, L.T., Ong, Y.S., Hoai, N.X., Ishibuchi, H., Suganthan, P.N. (eds) Simulated Evolution and Learning. SEAL 2012. Lecture Notes in Computer Science, vol 7673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34859-4_9

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  • DOI: https://doi.org/10.1007/978-3-642-34859-4_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34858-7

  • Online ISBN: 978-3-642-34859-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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