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Genetic Algorithm Based on Enhanced Selection and Log-Scaled Mutation Technique

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Proceedings of the Future Technologies Conference (FTC) 2018 (FTC 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 880))

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Abstract

In this paper, we introduce the selection and mutation schemes to enhance the computational power of Genetic Algorithm (GA) for global optimization of multi-modal problems. Proposed operators make the GA an efficient optimizer in comparison of other variants of GA with improved precision, consistency and diversity. Due to the presented selection and mutation schemes improved GA, as named Enhanced Selection and Log-scaled Mutation GA (ESALOGA), selects the best chromosomes from a pool of parents and children after crossover. Indeed, the proposed GA algorithm is adaptive due to the log-scaled mutation scheme, which corresponds to the fitness of current population at each stage of its execution. Our proposal is further supported via the simulation and comparative analysis with standard GA (SGA) and other variants of GA for a class of multi-variable objective functions. Additionally, comparative results with other optimizers such as Probabilistic Bee Algorithm (PBA), Invasive Weed Optimizer (IWO), and Shuffled Frog Leap Algorithm (SFLA) are presented on higher number of variables to show the effectiveness of ESALOGA.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Oakland University, Rochester, MI, USA

    Neeraj Gupta & Nilesh Patel

  2. INFN-Laboratori Nazionali di Frascati, Via. E. Fermi, 40 – I – 00044, Frascati, Rome, Italy

    Bhupendra Nath Tiwari

  3. Department of Electrical and Electronics Engineering, Fedral University of Juiz de Fora, Juiz de Fora, Brazil

    Mahdi Khosravy

Authors
  1. Neeraj Gupta
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  2. Nilesh Patel
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  3. Bhupendra Nath Tiwari
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  4. Mahdi Khosravy
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Corresponding author

Correspondence to Neeraj Gupta .

Editor information

Editors and Affiliations

  1. Saga University , Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

  3. The Science and Information (SAI) Organization, Bradford, UK

    Supriya Kapoor

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Gupta, N., Patel, N., Tiwari, B.N., Khosravy, M. (2019). Genetic Algorithm Based on Enhanced Selection and Log-Scaled Mutation Technique. In: Arai, K., Bhatia, R., Kapoor, S. (eds) Proceedings of the Future Technologies Conference (FTC) 2018. FTC 2018. Advances in Intelligent Systems and Computing, vol 880. Springer, Cham. https://doi.org/10.1007/978-3-030-02686-8_55

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