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Tracing the Points in Search Space in Plant Biology Genetics Algorithm Optimization

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Frontier Applications of Nature Inspired Computation

Part of the book series: Springer Tracts in Nature-Inspired Computing ((STNIC))

Abstract

A very recent meta-heuristic optimizer is by inspiration from plant biology where the Mendel law of heredity is implemented through multi-species in two generations. Plant biology-inspired optimizer named as Mendelian Evolutionary Optimization Algorithm (METO), which has several advantages outperforming the state-of-the-art optimizers. It is highly capable of finding the best solution for multimodal problems with global optimal solution and computationally fast. METO not only performs well over the problems with around thirty variables but also performs well on the very high-dimensional problems such as hundred variables. Besides the literature introducing the characteristics of the METO, this chapter investigates the way METO explores the search space of the problem by exchanging the gene’s information between the multi-species. Each plan in a species represents by double strands DNA. Here, we will observe how METO covers the search space and avoid being stuck in a local minimum and moves toward the global solution. In this chapter, we investigate the behavior of the operators of METO such as flipper, pollination, self- and cross-breeding, and epimutation.

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Author information

Authors and Affiliations

  1. Media Integrated Communication Lab, Graduate School of Engineering, Osaka University, Osaka, Japan

    Mahdi Khosravy

  2. Electrical Engineering Department, Federal University of Juiz de Fora, Juiz de Fora, Brazil

    Mahdi Khosravy

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

    Neeraj Gupta & Nilesh Patel

  4. Power System Planning Division, Rajasthan Rajya Vdhyut Prasaran Nigam Ltd., Jaipur, Rajasthan, India

    Om Prakash Mahela

  5. University of Information Science and Technology, Ohrid, North Macedonia

    Gazal Varshney

Authors
  1. Mahdi Khosravy
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  2. Neeraj Gupta
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  3. Nilesh Patel
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  4. Om Prakash Mahela
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  5. Gazal Varshney
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Corresponding author

Correspondence to Neeraj Gupta .

Editor information

Editors and Affiliations

  1. Media Integrated Communication Lab, Graduate School of Engineering, Osaka University, Osaka, Japan

    Mahdi Khosravy

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

    Neeraj Gupta

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

    Nilesh Patel

  4. Department of Electrical and Electronics Engineering, Faculty of Engineering, University of the Ryukyus, Nishihara, Japan

    Tomonobu Senjyu

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Khosravy, M., Gupta, N., Patel, N., Mahela, O.P., Varshney, G. (2020). Tracing the Points in Search Space in Plant Biology Genetics Algorithm Optimization. In: Khosravy, M., Gupta, N., Patel, N., Senjyu, T. (eds) Frontier Applications of Nature Inspired Computation. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-15-2133-1_8

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