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Introduction of a Mutation Specific Fast Non-dominated Sorting GA Evolved for Biochemical Optimizations

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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

In many physiochemical and biological phenomena, molecules have to comply with multiple optimized biophysical feature constraints. Mathematical modeling of these biochemical problems consequently results in multi-objective optimization. This study presents a special fast non-dominated sorting genetic algorithm (GA) incorporating different types of mutation (referred to as MSNSGA-II) for resolving multiple diverse requirements for molecule bioactivity with an early convergence in a comparable low number of generations. Hence, MSNSGA-II is based on a character codification and its performance is benchmarked via a specific three-dimensional optimization problem. Three objective functions are provided by the BioJava library: Needleman Wunsch algorithm, hydrophilicity and molecular weight. The performance of our proposed algorithm is tested using several mutation operators: A deterministic dynamic, a self-adaptive, a dynamic adaptive and two further mutation schemes with mutation rates based on the Gaussian distribution. Furthermore, we expose the comparison of MSNSGA-II with the classic NSGA-II in performance.

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Authors and Affiliations

  1. Faculty of Computer Science, Chair of Optimized Systems, University of Applied Sciences, FHDW, Hauptstr. 2, D-51465, Bergisch Gladbach, Germany

    Susanne Rosenthal, Nail El-Sourani & Markus Borschbach

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  1. Susanne Rosenthal
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  2. Nail El-Sourani
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  3. Markus Borschbach
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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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Rosenthal, S., El-Sourani, N., Borschbach, M. (2012). Introduction of a Mutation Specific Fast Non-dominated Sorting GA Evolved for Biochemical Optimizations. 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_16

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  • DOI: https://doi.org/10.1007/978-3-642-34859-4_16

  • 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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