Abstract
Real-coded Genetic Algorithms (RGAs) usually meet the demand of continuous and continuous/discreet mixed space problems and have been widely applied in many fields. The paper proposed a new Hybrid Real-coded Genetic Algorithm (NHRGA), in which the idea of Particle Swarm Optimization (PSO) is introduced into mutation operator and physics field theory is also employed in algorithm operators. The NHRGA reduces the possibility of trapping into the local optimal solutions and improves the computation efficiency. A practical engineering example is given to demonstrate computation efficiency and robustness of the proposed method.
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Wang, Z., Xiong, J., Miao, Q., Yang, B., Ling, D. (2006). New Hybrid Real-Coded Genetic Algorithm. In: Sattar, A., Kang, Bh. (eds) AI 2006: Advances in Artificial Intelligence. AI 2006. Lecture Notes in Computer Science(), vol 4304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941439_151
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DOI: https://doi.org/10.1007/11941439_151
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49787-5
Online ISBN: 978-3-540-49788-2
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