Evolutionary Field Optimization (EFO)
Evolutionary Field Optimization is a population-based metaheuristic optimization algorithm that implements the evolutionary field theorem.
You are now following this Submission
- You will see updates in your followed content feed
- You may receive emails, depending on your communication preferences
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
Cite As
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
General Information
- Version 1.0.2 (1.97 MB)
MATLAB Release Compatibility
- Compatible with R2014b to R2023b
Platform Compatibility
- Windows
- macOS
- Linux
