Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Modeling of dynamic systems
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A comprehensive survey of fitness approximation in evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Exploiting gradient information in numerical multi--objective evolutionary optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Combining gradient techniques for numerical multi-objective evolutionary optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Local search for multiobjective function optimization: pareto descent method
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Multi-Objective Optimization using Grid Computing
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
An informed convergence accelerator for evolutionary multiobjective optimiser
Proceedings of the 9th annual conference on Genetic and evolutionary computation
An analysis of the effects of population structure on scalable multiobjective optimization problems
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A study on metamodeling techniques, ensembles, and multi-surrogates in evolutionary computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Computational Optimization and Applications
Effective use of directional information in multi-objective evolutionary computation
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
On the performance of metamodel assisted MOEA/D
ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence
Accelerating evolutionary algorithms with Gaussian process fitness function models
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Multiobjective GA optimization using reduced models
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Combining Global and Local Surrogate Models to Accelerate Evolutionary Optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A framework for evolutionary optimization with approximate fitnessfunctions
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Max-min surrogate-assisted evolutionary algorithm for robust design
IEEE Transactions on Evolutionary Computation
Single- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels
IEEE Transactions on Evolutionary Computation
A review of multiobjective test problems and a scalable test problem toolkit
IEEE Transactions on Evolutionary Computation
Towards high speed multiobjective evolutionary optimizers
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
The Pareto-following variation operator as an alternative approximation model
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Comprehensive Survey of the Hybrid Evolutionary Algorithms
International Journal of Applied Evolutionary Computation
Hi-index | 0.00 |
One of the major difficulties when applying Multiobjective Evolutionary Algorithms (MOEA) to real world problems is the large number of objective function evaluations. Approximate (or surrogate) methods offer the possibility of reducing the number of evaluations, without reducing solution quality. Artificial Neural Network (ANN) based models are one approach that have been used to approximate the future front from the current available fronts with acceptable accuracy levels. However, the associated computational costs limit their effectiveness. In this work, we introduce a simple approach that has comparatively smaller computational cost and we have developed this model as a variation operator that can be used in any kind of multiobjective optimizer. When designing this model, we have considered the whole search procedure as a dynamic system that takes available objective values in current front as input and generates approximated design variables for the next front as output. Initial simulation experiments have produced encouraging results in comparison to NSGA-II. Our motivation was to increase the speed of the hosting optimizer. We have compared the performance of the algorithm with respect to the total number of function evaluation and Hypervolume metric. This variation operator has worst case complexity of O(nkN3), where N is the population size, n and k is the number of design variables and objectives respectively.