Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
An investigation of niche and species formation in genetic function optimization
Proceedings of the third international conference on Genetic algorithms
A species conserving genetic algorithm for multimodal function optimization
Evolutionary Computation
A detecting peak's number technique for multimodal function optimization
EC'07 Proceedings of the 8th Conference on 8th WSEAS International Conference on Evolutionary Computing - Volume 8
A detecting peak's number technique for multimodal function optimization
WSEAS Transactions on Information Science and Applications
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
| Hi-index | 0.00 |
We propose a selection scheme called Fitness-based Neighbor Selection (FNS) for multimodal optimization. The FNS is aimed for ill-scaled and locally multimodal domain, both found in real-world numerical optimization problem.In FNS, selection is applied to parent-child pair that most likely belong to the same attractor. We determine such pair with statistical comparison of the fitness values sampled from region between the pairs, instead of conventional Euclidean distance. In addition, the ranks of a parent among sampled values are used to determine if the parent is replaceable. These measurements makes the algorithm scale-invariant thus robust in ill-scaled domain.