Evolutionary Field Optimization (EFO)

Version 1.0.2 (1.97 MB) by Baris Baykant ALAGOZ
Evolutionary Field Optimization is a population-based metaheuristic optimization algorithm that implements the evolutionary field theorem.
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Updated 1 May 2024

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Evolutionary Field Optimization with Geometric Strategies (EFO-GS) is based on the evolutionary field theorem of search agents. The EFO-GS uses a field-adapted differential crossover mechanism and a field-aware metamutation process in order to improve the evolutionary search quality.
Citation: Alagoz BB, Simsek OI, Ari D, Tepljakov A, Petlenkov E, Alimohammadi H. An Evolutionary Field Theorem: Evolutionary Field Optimization in Training of Power-Weighted Multiplicative Neurons for Nitrogen Oxides-Sensitive Electronic Nose Applications. Sensors. 2022; 22(10):3836. https://doi.org/10.3390/s22103836

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Alagoz BB, Simsek OI, Ari D, Tepljakov A, Petlenkov E, Alimohammadi H. An Evolutionary Field Theorem: Evolutionary Field Optimization in Training of Power-Weighted Multiplicative Neurons for Nitrogen Oxides-Sensitive Electronic Nose Applications. Sensors. 2022; 22(10):3836. https://doi.org/10.3390/s22103836

MATLAB Release Compatibility
Created with R2014b
Compatible with R2014b to R2023b
Platform Compatibility
Windows macOS Linux
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Version Published Release Notes
1.0.2

The ReadMe file is improved.
1.0.1

The ReadMe document was improved.
1.0.0