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”. Sec...

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Veröffentlicht in:Soft computing (Berlin, Germany) Jg. 24; H. 19; S. 14345 - 14390
Hauptverfasser: Gupta, Neeraj, Khosravy, Mahdi, Patel, Nilesh, Dey, Nilanjan, Mahela, Om Prakash
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2020
Springer Nature B.V
Schlagworte:
Artificial Intelligence
Biogeography
Biological evolution
Composite functions
Computational Intelligence
Control
Covariance matrix
Denaturation
Deoxyribonucleic acid
DNA
Engineering
Evolutionary algorithms
Evolutionary computation
Foraging behavior
Foundations
Genes
Genetic algorithms
Heuristic
Heuristic methods
Hybrid composites
Machine learning
Mathematical analysis
Mathematical Logic and Foundations
Mechatronics
Mutation
Operators (mathematics)
Optimization
Optimization algorithms
Optimization techniques
Plant reproduction
Robotics
Search algorithms
Seeds
Statistical analysis
Variables
Multi-species
Mendelian evolutionary theory
Meta-heuristic optimization
Artificial DNA
Binary coded optimizer
Rehabilitation
Pollination
ISSN:1432-7643, 1433-7479
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Zusammenfassung: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 Epimutation , 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, (2) bio-geography-based optimization, (3) invasive weed optimization, (4) shuffled frog leap algorithm, (5) teaching–learning-based optimization, (6) cuckoo search, (7) bat algorithm, (8) gravitational search algorithm, (9) covariance matrix adaptation evolution strategy, (10) differential evolution, (11) firefly algorithm and (12) social learning PSO. This comparison is evaluated on 30 and 100 variables benchmark test functions, including noisy, rotated, and hybrid composite functions. Kruskal–Wallis statistical rank-based nonparametric 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.
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-020-05239-2