Multilayer feedforward networks are universal approximators
Neural Networks
Instance-Based Learning Algorithms
Machine Learning
The computational brain
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
Comparison and evaluation of multiple objective genetic algorithms for the antenna placement problem
Mobile Networks and Applications
The Fast Evaluation Strategy for Evolvable Hardware
Genetic Programming and Evolvable Machines
Neural network classification of homomorphic segmented heart sounds
Applied Soft Computing
Review: Neural networks and statistical techniques: A review of applications
Expert Systems with Applications: An International Journal
Nature-Inspired Algorithms for Optimisation
Nature-Inspired Algorithms for Optimisation
Performance scaling of multi-objective evolutionary algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
The measure of Pareto optima applications to multi-objective metaheuristics
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Computational Intelligence in Expensive Optimization Problems
Computational Intelligence in Expensive Optimization Problems
Handling drifts and shifts in on-line data streams with evolving fuzzy systems
Applied Soft Computing
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
Comparison of neural classifiers for vehicles gear estimation
Applied Soft Computing
jMetal: A Java framework for multi-objective optimization
Advances in Engineering Software
Variants of Evolutionary Algorithms for Real-World Applications
Variants of Evolutionary Algorithms for Real-World Applications
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems
IEEE Transactions on Evolutionary Computation
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Performance optimization of electrical drives implies a lot of degrees of freedom in the variation of design parameters, which in turn makes the process overly complex and sometimes impossible to handle for classical analytical optimization approaches. This, and the fact that multiple non-independent design parameter have to be optimized synchronously, makes a soft computing approach based on multi-objective evolutionary algorithms (MOEAs) a feasible alternative. In this paper, we describe the application of the well known Non-dominated Sorting Genetic Algorithm II (NSGA-II) in order to obtain high-quality Pareto-optimal solutions for three optimization scenarios. The nature of these scenarios requires the usage of fitness evaluation functions that rely on very time-intensive finite element (FE) simulations. The key and novel aspect of our optimization procedure is the on-the-fly automated creation of highly accurate and stable surrogate fitness functions based on artificial neural networks (ANNs). We employ these surrogate fitness functions in the middle and end parts of the NSGA-II run (-hybridization) in order to significantly reduce the very high computational effort required by the optimization process. The results show that by using this hybrid optimization procedure, the computation time of a single optimization run can be reduced by 46-72% while achieving Pareto-optimal solution sets with similar, or even slightly better, quality as those obtained when conducting NSGA-II runs that use FE simulations over the whole run-time of the optimization process.