Clustering methods for large scale geometrical global optimization

Bagattini, Francesco; Schoen, Fabio; Tigli, Luca

doi:10.1080/10556788.2019.1582651

Cited by 9 publications

(1 citation statement)

References 30 publications

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“…EFO-GS algorithm implements a hybrid search methodology that aims to benefit from advantages of the geometrical search strategies in the evolutionary field to improve the differential evolution processes. Effective geometrical search methods have been shown to convergence to minimum points [ 56 , 57 , 58 ]. The EFO-GS algorithm evolves an initial property code

towards the seasonal best of codes (

, the parameter

is the number of agent population) by repeatedly performing advantageous seasonal evolution of the property code according to scattering geometry of agent’s property codes within the evolution field.…”

Section: Evolutionary Field Searchmentioning

confidence: 99%

An Evolutionary Field Theorem: Evolutionary Field Optimization in Training of Power-Weighted Multiplicative Neurons for Nitrogen Oxides-Sensitive Electronic Nose Applications

Alagöz

Simsek

Ari

et al. 2022

Sensors

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Neuroevolutionary 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.

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towards the seasonal best of codes (

, the parameter

Section: Evolutionary Field Searchmentioning

confidence: 99%