Alagoz, Baris BaykantSimsek, Ozlem ImikAri, DavutTepljakov, AlekseiPetlenkov, EduardAlimohammadi, Hossein2024-08-042024-08-0420221424-8220https://doi.org/10.3390/s22103836https://hdl.handle.net/11616/100657Neuroevolutionary machine learning is an emerging topic in the evolutionary computation field and enables practical modeling solutions for data-driven engineering applications. Contributions of this study to the neuroevolutionary machine learning area are twofold: firstly, this study presents an evolutionary field theorem of search agents and suggests an algorithm for Evolutionary Field Optimization with Geometric Strategies (EFO-GS) on the basis of the evolutionary field theorem. The proposed EFO-GS algorithm benefits from a field-adapted differential crossover mechanism, a field-aware metamutation process to improve the evolutionary search quality. Secondly, the multiplicative neuron model is modified to develop Power-Weighted Multiplicative (PWM) neural models. The modified PWM neuron model involves the power-weighted multiplicative units similar to dendritic branches of biological neurons, and this neuron model can better represent polynomial nonlinearity and they can operate in the real-valued neuron mode, complex-valued neuron mode, and the mixed-mode. In this study, the EFO-GS algorithm is used for the training of the PWM neuron models to perform an efficient neuroevolutionary computation. Authors implement the proposed PWM neural processing with the EFO-GS in an electronic nose application to accurately estimate Nitrogen Oxides (NOx) pollutant concentrations from low-cost multi-sensor array measurements and demonstrate improvements in estimation performance.eninfo:eu-repo/semantics/openAccessneuroevolutionevolutionary optimizationmultiplicative neuron modelconcentration estimationelectronic noseIndustry 40Article22103563224510.3390/s221038362-s2.0-85130267279Q1WOS:000801755200001Q2