A Rosetta stone for connectionism
CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
A comparison of Q-learning and classifier systems
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Adding temporary memory to ZCS
Adaptive Behavior
Learning to solve multiple goals
Learning to solve multiple goals
Adaptive Behavior
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Representational Difficulties with Classifier Systems
Proceedings of the 3rd International Conference on Genetic Algorithms
Mapping Neural Networks into Classifier Systems
Proceedings of the 3rd International Conference on Genetic Algorithms
Strength or Accuracy? Fitness Calculation in Learning Classifier Systems
Learning Classifier Systems, From Foundations to Applications
State of XCS Classifier System Research
Learning Classifier Systems, From Foundations to Applications
Toward Optimal Classifier System Performance in Non-Markov Environments
Evolutionary Computation
Adding learning to the cellular development of neural networks: Evolution and the baldwin effect
Evolutionary Computation
Zcs: A zeroth level classifier system
Evolutionary Computation
Is a learning classifier system a type of neural network?
Evolutionary Computation
Classifier fitness based on accuracy
Evolutionary Computation
Forming neural networks through efficient and adaptive coevolution
Evolutionary Computation
On Lookahead and Latent Learning in Simple LCS
Learning Classifier Systems
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For effective use in a number of problem domains Learning Classifier Systems must be able to manage multiple objectives. This paper explicitly considers the case of developing the controller for a simulated mobile autonomous robot which must achieve a given task whilst maintaining sufficient battery power. A form of Learning Classifier System in which each rule is represented by an artificial neural network is used. Results are presented which show it is possible to solve both objectives when the energy level is presented as an input along with sensor data. A more realistic, and hence more complex, version of the basic scenario is then investigated.