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\begin{document}

\title{Mendelian Evolutionary Theory Optimization Algorithm}



\author[1]{Neeraj Gupta }%
\author[2]{Mahdi Khosravy }%
\author[1]{Nilesh Patel }%
\author[1]{Nilanjan Dey }%
\author[1]{Om Prakash Mahela }%
\affil[1]{Affiliation not available}%
\affil[2]{Osaka University}%


\vspace{-1em}



  
  \date{June 14, 2024}


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\begin{abstract}
This study presented a new multi-species binary coded algorithm,
Mendelian Evolutionary Theory Optimization (METO), inspired by the plant
genetics. This framework mainly consists of three concepts: First, the
``denaturation'' of DNA's of two different species to produce the hybrid
``offspring DNA''. Second , the Mendelian evolutionary theory of genetic
inheritance, which explains how the dominant and recessive traits appear
in two successive generations. Third, the Epimuation, through which
organism resist for natural mutation. The above concepts are
reconfigured in order to design the binary meta-heuristic evolutionary
search technique. Based on this framework, four evolutionary operators
-- 1) Flipper, 2) Pollination, 3) Breeding, and 4) Epimutation -- are
created in the binary domain. In this paper, METO is compared with
well-known evolutionary and swarm optimizers 1) Binary Hybrid GA (BHGA),
2) Bio-geography Based Optimization (BBO), 3) Invasive Weed Optimization
(IWO), 4) Shuffled Frog Leap Algorithm (SFLA), 5) Teaching-Learning
Based Optimization (TLBO), 6) Cuckoo Search (CS), 7) Bat Algorithm (BA),
8) Gravitational Search Algorithm (GSA), 9) Covariance Matrix Adaptation
Evolution Strategy(CMAES), 10) Differential Evolution (DE), 11) Firefly
Algorithm (FA) and 12) Social Learning PSO (SLPSO). This comparison is
evaluated on 30 and 100 variables benchmark test functions, including
noisy, rotated, and hybrid composite functions. Kruskal Wallis
statistical rank-based non-parametric H-test is utilized to determine
the statistically significant differences between the output
distributions of the optimizer, which are the result of the 100
independent runs. The statistical analysis shows that METO is a
significantly better algorithm for complex and multi-modal problems with
many local extremes.%
\end{abstract}%



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