Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
An introduction to genetic algorithms
An introduction to genetic algorithms
Niching methods for genetic algorithms
Niching methods for genetic algorithms
Growing artificial societies: social science from the bottom up
Growing artificial societies: social science from the bottom up
Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Niching in Monte Carlo Filtering Algorithms
Selected Papers from the 5th European Conference on Artificial Evolution
Robust global localization using clustered particle filtering
Eighteenth national conference on Artificial intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Efficient and Scalable Pareto Optimization by Evolutionary Local Selection Algorithms
Evolutionary Computation
Expert Systems with Applications: An International Journal
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Monte-Carlo localization uses particle filtering to estimate the position of the robot. The method is known to suffer from the loss of potential positions when there is ambiguity present in the environment. Since many indoor environments are highly symmetric, this problem of premature convergence is problematic for indoor robot navigation. It is, however, rarely studied in particle filters. We introduce a number of so-called niching methods used in genetic algorithms, and implement them on a particle filter for Monte-Carlo localization. The experiments show a significant improvement in the diversity maintaining performance of the particle filter.