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Julia and Singularity for High Performance Computing Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Improvement of Island Genetic Algorithm Using Multiple Fitness Functions

Authors : Shigeka Nakajima, Masami Takata

Published in: Advances in Parallel & Distributed Processing, and Applications

Publisher: Springer International Publishing

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Abstract

In this paper, we propose an island genetic algorithm (GA) that promotes a unique evolution. In a conventional island GA [3], all objective functions are combined into a single fitness function. Hence, offspring generations are generated using the same fitness function. In the natural world, each should evolve in a manner that suits the environment, and owing to the various environments on Earth, various organisms have been diversified. Therefore, we propose an improved island GA with different fitness functions to create a distinctive evolution.

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previous chapter On an Implementation of the One-Sided Jacobi Method with High Accuracy
next chapter High-Performance Cloud Computing for Exhaustive Protein–Protein Docking
Literature
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B.S. Baker, E.G. Coffman, R.L. Rivest Jr., Orthogonal packing in two dimensions. SIAM J. Comput. 9, 846–855 (1980)MathSciNetCrossRef
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T. Fujita, Y. Numata, T. Yoshimi, A study of intelligent drawer with RFID tag information reading system for intelligent space, in Proceedings of 2012 IEEE International Conference on Mechatronics and Automation (2012), pp. 533–538
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D.E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning (Addison-Wesley, Reading, 1989)MATH
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N. Yano, T. Morinaga, T. Saito, Packing optimization for cargo containers. SICE Annu. Conf. 2008, 3479–3482 (2008)
Metadata
Title
Improvement of Island Genetic Algorithm Using Multiple Fitness Functions
Authors
Shigeka Nakajima
Masami Takata
Copyright Year
2021
Book
Advances in Parallel & Distributed Processing, and Applications

Print ISBN: 978-3-030-69983-3

Electronic ISBN: 978-3-030-69984-0

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-69984-0_52