Mendelian evolutionary theory optimization algorithm

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.

[1]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[2]  Nikolaus Hansen,et al.  The CMA Evolution Strategy: A Comparing Review , 2006, Towards a New Evolutionary Computation.

[3]  Xin-She Yang,et al.  Bat algorithm for multi-objective optimisation , 2011, Int. J. Bio Inspired Comput..

[4]  Dan Simon,et al.  Biogeography-Based Optimization , 2022 .

[5]  Y. Chan,et al.  Learning and understanding the Kruskal-Wallis one-way analysis-of-variance-by-ranks test for differences among three or more independent groups. , 1997, Physical therapy.

[6]  Christian Igel,et al.  No Free Lunch Theorems: Limitations and Perspectives of Metaheuristics , 2014, Theory and Principled Methods for the Design of Metaheuristics.

[7]  Manju Khari,et al.  Performance analysis of six meta-heuristic algorithms over automated test suite generation for path coverage-based optimization , 2020, Soft Comput..

[8]  J. Crow.  The genetic basis of evolutionary change , 1975 .

[9]  Andrew Travers,et al.  DNA structure and function , 2015, The FEBS journal.

[10]  Duc-Cuong Dang,et al.  Escaping Local Optima with Diversity Mechanisms and Crossover , 2016, GECCO.

[11]  Vilém Novák,et al.  Algebraic analysis of fuzzy systems , 2007, Fuzzy Sets Syst..

[12]  Raffaele Cerulli,et al.  Models, algorithms and applications for location problems , 2016, Optim. Lett..

[13]  Ramin Rajabioun,et al.  Cuckoo Optimization Algorithm , 2011, Appl. Soft Comput..

[14]  Raffaele Cerulli,et al.  An evolutionary approach for the offsetting inventory cycle problem , 2017 .

[15]  Erik Valdemar Cuevas Jiménez,et al.  A hybrid evolutionary approach based on the invasive weed optimization and estimation distribution algorithms , 2019, Soft Comput..

[16]  Per Kristian Lehre,et al.  Escaping Local Optima Using Crossover With Emergent Diversity , 2018, IEEE Transactions on Evolutionary Computation.

[17]  C. Farhat.  International Journal for Numerical Methods in Engineering , 2019 .

[18]  Surya N. Patnaik,et al.  COMPARATIVE EVALUATION OF DIFFERENT OPTIMIZATION ALGORITHMS FOR STRUCTURAL DESIGN APPLICATIONS , 1996 .

[19]  L.A.-C.P. Martins.  The dissemination of the chromosome theory of Mendelian heredity by Morgan and his collaborators around 1915: a case study on the distortion of science by scientists , 2010 .

[20]  Kay Chen Tan,et al.  A Multi-Facet Survey on Memetic Computation , 2011, IEEE Transactions on Evolutionary Computation.

[21]  Bo Xing,et al.  Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms , 2013 .

[22]  Caro Lucas,et al.  A novel numerical optimization algorithm inspired from weed colonization , 2006, Ecol. Informatics.

[23]  Corrado Nai,et al.  Let microorganisms do the talking, let us talk more about microorganisms , 2016, Fungal Biology and Biotechnology.

[24]  Layne T. Watson,et al.  A genetic algorithm with memory for optimal design of laminated sandwich composite panels , 2002 .

[25]  Yu Liu,et al.  A novel bat algorithm with habitat selection and Doppler effect in echoes for optimization , 2015, Expert Syst. Appl..

[26]  Nilanjan Dey,et al.  Economic data analytic AI technique on IoT edge devices for health monitoring of agriculture machines , 2020, Applied Intelligence.

[27]  Om Prakash Mahela,et al.  Plant Biology-Inspired Genetic Algorithm: Superior Efficiency to Firefly Optimizer , 2019, Springer Tracts in Nature-Inspired Computing.

[28]  Yaochu Jin,et al.  A social learning particle swarm optimization algorithm for scalable optimization , 2015, Inf. Sci..

[29]  Ponnuthurai N. Suganthan,et al.  An Adaptive Differential Evolution Algorithm With Novel Mutation and Crossover Strategies for Global Numerical Optimization , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[30]  Sebastián Lozano,et al.  Metaheuristic optimization frameworks: a survey and benchmarking , 2011, Soft Computing.

[31]  Zhen Liu,et al.  Memetic frog leaping algorithm for global optimization , 2018, Soft Computing.

[32]  Selim Yilmaz,et al.  A new modification approach on bat algorithm for solving optimization problems , 2015, Appl. Soft Comput..

[33]  Xin-She Yang,et al.  Engineering optimisation by cuckoo search , 2010, Int. J. Math. Model. Numer. Optimisation.

[34]  Petros Koumoutsakos,et al.  Reducing the Time Complexity of the Derandomized Evolution Strategy with Covariance Matrix Adaptation (CMA-ES) , 2003, Evolutionary Computation.

[35]  P. N. Suganthan,et al.  Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization , 2009, IEEE Transactions on Evolutionary Computation.

[36]  Raffaele Cerulli,et al.  Comparison of heuristics for the colourful travelling salesman problem , 2013, Int. J. Metaheuristics.

[37]  Professor Dr. Rafael Frankel,et al.  Pollination Mechanisms, Reproduction and Plant Breeding , 1977, Monographs on Theoretical and Applied Genetics.

[38]  Yang Lou,et al.  Selecting evolutionary algorithms for black box design optimization problems , 2018, Soft Comput..

[39]  Tomonobu Senjyu,et al.  A Bi-Level Evolutionary Optimization for Coordinated Transmission Expansion Planning , 2018, IEEE Access.

[40]  Applied Nature-Inspired Computing: Algorithms and Case Studies , 2020, Springer Tracts in Nature-Inspired Computing.

[41]  Shengxiang Yang,et al.  Genetic Algorithms with Memory- and Elitism-Based Immigrants in Dynamic Environments , 2008, Evolutionary Computation.

[42]  Z. Beheshti.  A review of population-based meta-heuristic algorithm , 2013, SOCO 2013.

[43]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[44]  Xin-She Yang,et al.  Firefly Algorithm, Lévy Flights and Global Optimization , 2010, SGAI Conf..

[45]  Ye Liang,et al.  Applying genetic algorithm and ant colony optimization algorithm into marine investigation path planning model , 2020, Soft Comput..

[46]  E. Whitelaw,et al.  On the meaning of the word 'epimutation'. , 2014, Trends in genetics : TIG.

[47]  Stefan Voß,et al.  Metaheuristics Comparison for the Minimum Labelling Spanning Tree Problem , 2005 .

[48]  Muzaffar Eusuff,et al.  Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization , 2006 .

[49]  Amir Hossein Gandomi,et al.  Chaotic bat algorithm , 2014, J. Comput. Sci..

[50]  Neeraj Gupta,et al.  Evolutionary Artificial Neural Networks: Comparative Study on State-of-the-Art Optimizers , 2020 .

[51]  Patrick Siarry,et al.  A survey on optimization metaheuristics , 2013, Inf. Sci..

[52]  Joshua A. Granek,et al.  Antifungal drug resistance evokedvia RNAi-dependent epimutations , 2014, Nature.

[53]  R. Venkata Rao,et al.  Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems , 2011, Comput. Aided Des..

[54]  Lei Liu,et al.  Particle swarm optimization algorithm: an overview , 2017, Soft Computing.

[55]  Tomonobu Senjyu,et al.  Particle Swarm Optimization of Morphological Filters for Electrocardiogram Baseline Drift Estimation , 2019, Applied Nature-Inspired Computing: Algorithms and Case Studies.

[56]  P. N. Suganthan,et al.  Differential Evolution: A Survey of the State-of-the-Art , 2011, IEEE Transactions on Evolutionary Computation.

[57]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[58]  R. Lewontin,et al.  The Genetic Basis of Evolutionary Change , 2022 .

[59]  Layne T. Watson,et al.  COMPOSITE LAMINATE DESIGN OPTIMIZATION BY GENETIC ALGORITHM WITH GENERALIZED ELITIST SELECTION , 2001 .

[60]  Frontier Applications of Nature Inspired Computation , 2020, Springer Tracts in Nature-Inspired Computing.

[61]  W. Paszkowicz,et al.  Genetic Algorithms, a Nature-Inspired Tool: A Survey of Applications in Materials Science and Related Fields: Part II , 2009 .

[62]  E. Hamilton-Smith.  Nature by Design: People, Natural Process and Ecological Restoration , 2004 .

[63]  Rolf Wanka,et al.  Explanation of Stagnation at Points that are not Local Optima in Particle Swarm Optimization by Potential Analysis , 2015, GECCO.

[64]  G. Stebbins.  Self Fertilization and Population Variability in the Higher Plants , 1957, The American Naturalist.

[65]  Ilsoo Yun,et al.  Comparative evaluation of heuristic optimization methods in urban arterial network optimization , 2009, 2009 12th International IEEE Conference on Intelligent Transportation Systems.

[66]  Neeraj Gupta,et al.  Genetic Algorithm Based on Enhanced Selection and Log-Scaled Mutation Technique , 2018 .

[67]  Mohamed E. El-Hawary,et al.  A Survey of Particle Swarm Optimization Applications in Electric Power Systems , 2009, IEEE Transactions on Evolutionary Computation.