Communications of the ACM
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
Computational limitations on learning from examples
Journal of the ACM (JACM)
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic and evolutionary algorithms come of age
Communications of the ACM
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
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
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Searching for diverse, cooperative populations with genetic algorithms
Evolutionary Computation
A markov chain framework for the simple genetic algorithm
Evolutionary Computation
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
Grouping Character Shapes by Means of Genetic Programming
IWVF-4 Proceedings of the 4th International Workshop on Visual Form
A Learning Classifier Systems Bibliography
Learning Classifier Systems, From Foundations to Applications
A Roadmap to the Last Decade of Learning Classifier System Research
Learning Classifier Systems, From Foundations to Applications
Simple Markov Models of the Genetic Algorithm in Classifier Systems: Accuracy-Based Fitness
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
Simple Markov Models of the Genetic Algorithm in Classifier Systems: Multi-step Tasks
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
A Bigger Learning Classifier Systems Bibliography
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
Optimizing RBF Networks with Cooperative/Competitive Evolution of Units and Fuzzy Rules
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Introducing Multi-objective Optimization in Cooperative Coevolution of Neural Networks
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
MOLeCS: Using Multiobjective Evolutionary Algorithms for Learning
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Resource-Based Fitness Sharing
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Do We Really Need to Estimate Rule Utilities in Classifier Systems?
Learning Classifier Systems, From Foundations to Applications
Neural Networks - 2006 special issue: Earth sciences and environmental applications of computational intelligence
Classifier fitness based on accuracy
Evolutionary Computation
Forming neural networks through efficient and adaptive coevolution
Evolutionary Computation
An analysis of the “universal suffrage” selection operator
Evolutionary Computation
Accelerated Neural Evolution through Cooperatively Coevolved Synapses
The Journal of Machine Learning Research
Glowworm swarm optimisation: a new method for optimising multi-modal functions
International Journal of Computational Intelligence Studies
Natural niching for evolving cooperative classifiers
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
The paradox of the plankton: oscillations and chaos in multispecies evolution
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
ADA'04 Proceedings of the 3rd international conference on Astronomical Data Analysis
Self organizing classifiers and niched fitness
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Many hands make light work: Further studies in group evolution
Artificial Life
Hi-index | 0.00 |
We approach the difficult task of analyzing the complex behavior of even the simplest learning classifier system (LCS) by isolating one crucial subfunction in the LCS learning algorithm: covering through niching. The LCS must maintain a population of diverse rules that together solve a problem (e.g., classify examples). To maintain a diverse population while applying the GAs selection operator, the LCS 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). This implicit LCS fitness sharing is similar to the explicit fitness sharing used in many niched GAs. Indeed, the LCS implicit sharing algorithm can be mapped onto explicit fitness sharing with a one-to-one correspondence between algorithm components. This mapping is important because several studies of explicit fitness sharing, and of niching in GAs generally, have produced key insights and analytical tools for understanding the interaction of the niching and selection forces. We can now bring those results to bear in understanding the fundamental type of cooperation (a.k.a. weak cooperation) that an LCS must promote.