NETLAB: algorithms for pattern recognition
NETLAB: algorithms for pattern recognition
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
Towards high speed multiobjective evolutionary optimizers
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
A pareto following variation operator for fast-converging multiobjective evolutionary algorithms
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Convergence acceleration operator for multiobjective optimization
IEEE Transactions on Evolutionary Computation
The Pareto-following variation operator as an alternative approximation model
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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A novel optimisation accelerator deploying neural network predictions and objective space direct manipulation strategies is presented. The concept of directing the search through the use of 'mirage' solutions is introduced and investigated. The accelerator is meant to be a portable component that can be plugged into any stochastic optimisation algorithm, such as genetic algorithms. The purpose of the new component termed as the Informed Convergence Accelerator (ICA) is to enhance the search capability, convergence extent and most especially the speed of convergence of the hosting stochastic global optimisation technique. ICA was hybridized with the Non-Dominated Sorting Genetic Algorithm (NSGA-II). Enhanced results were achieved demonstrating the utility of the introduced component.