Niching in Monte Carlo Filtering Algorithms

Authors:
Alexis Bienvenüe;Marc Joannides;Jean Bérard;Éric Fontenas;Olivier François
Affiliations:
-;-;-;-;-
Venue:
Selected Papers from the 5th European Conference on Artificial Evolution
Year:
2001

Citing 4
Cited 1

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 3rd International Conference on Genetic Algorithms
The nature of niching: genetic algorithms and the evolution of optimal, cooperative populations

The nature of niching: genetic algorithms and the evolution of optimal, cooperative populations
An overview of evolutionary algorithms in multiobjective optimization

Evolutionary Computation

Tackling the premature convergence problem in Monte-Carlo localization

Robotics and Autonomous Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Nonlinear multimodal filtering problems are usually addressed via Monte Carlo algorithms. These algorithms involve sampling procedures that are similar to proportional selection in genetic algorithms, and that are prone to failure due to genetic drift. This work investigates the feasibility and the relevance of niching strategies in this context. Sharing methods are evaluated experimentally, and prove to be efficient in such issues.