Finite Markov chain analysis of genetic algorithms
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
Using genetic algorithms to learn disjunctive rules from examples
Proceedings of the seventh international conference (1990) on Machine learning
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Niching methods for genetic algorithms
Niching methods for genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Classifier Systems and the Animat Problem
Machine Learning
Properties of the Bucket Brigade
Proceedings of the 1st International Conference on Genetic Algorithms
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
A Study of Rule Set Development in a Learning Classifier System
Proceedings of the 3rd International Conference on Genetic Algorithms
Triggered Rule Discovery in Classifier Systems
Proceedings of the 3rd International Conference on Genetic Algorithms
A Critical Review of Classifier Systems
Proceedings of the 3rd International Conference on Genetic Algorithms
Finite Markov Chain Analysis of Genetic Algorithms with Niching
Proceedings of the 5th International Conference on Genetic Algorithms
Simple Analytical Models of Genetic Algorithms for Multimodal Function Optimization
Proceedings of the 5th International Conference on Genetic Algorithms
Analysis of Genetic Algorithms Evolution under Pure Selection
Proceedings of the 6th International Conference on Genetic Algorithms
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Intelligent behavior as an adaptation to the task environment
Intelligent behavior as an adaptation to the task environment
Searching for diverse, cooperative populations with genetic algorithms
Evolutionary Computation
Zcs: A zeroth level classifier system
Evolutionary Computation
Implicit niching in a learning classifier system: Nature's way
Evolutionary Computation
Classifier fitness based on accuracy
Evolutionary Computation
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
A Bigger Learning Classifier Systems Bibliography
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
Recent trends in learning classifier systems research
Advances in evolutionary computing
Review: The use of computational intelligence in intrusion detection systems: A review
Applied Soft Computing
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
Rethinking multilevel selection in genetic programming
Proceedings of the 13th annual conference on Genetic and evolutionary computation
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
| Hi-index | 0.00 |
An evolutionary classifier, such as a learning classifier system (LCS) or a genetic programming boolean concept learner, must maintain a population of diverse rules that together solve a problem (e.g., classify examples). To maintain "cooperative diversity" while applying a selection operator to the population of rules, as in the Michigan-style LCS, the evolutionary algorithm must incorporate some kind of niching mechanism. The natural way to accomplish niching in an LCS is to force competing rules to share resources (i.e., rewards). The implicit or "natural" niching and speciation induced by such resource sharing, is shown to be robust in the face of severe selective pressure, low population sizes, and overlapping rule coverage. Specifically in this paper we analyze the two-niche (two competing/cooperating rules) case. We find closed form approximations for niche maintenance and niche convergence times, giving us the beginnings of a first predictive model for interacting (cooperating) rules in an evolving population. Finally, we make the case for niching/speciation as a basic, indirect form of cooperation that is fundamental to, and underlying, all other types of more direct cooperation, and which the LCS must therefore promote. Although we focus on the LCS as an example of a specific and well-known evolutionary classifier, all of our results are general enough to apply to any evolutionary algorithm, such as genetic programming (GP), that applies selection to a population of diverse classifiers.