Lookahead planning and latent learning in a classifier system
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Artificial economic life: a simple model of a stockmarket
Proceedings of the NATO advanced research workshop and EGS topical workshop on Chaotic advection, tracer dynamics and turbulent dispersion
New challenges for evolutionary music and art
ACM SIGEVOlution
Portfolio allocation using XCS experts in technical analysis, market conditions and options market
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Initial results from the use of learning classifier systems to control in vitro neuronal networks
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Rule discovery in epidemiologic surveillance data using EpiXCS: an evolutionary computation approach
AIME'05 Proceedings of the 10th conference on Artificial Intelligence in Medicine
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Broadly conceived as computational models of cognition and tools for modeling complex adaptive systems, later extended for use in adaptive robotics, and today also applied to effective classification and data-mining--what is a learning classifier system? How does it work? What's the theory behind its functioning? What are the most interesting research directions? What the applications? And what the relevant open issues? This introductory tutorial tries to answer these questions. It provides a gentle introduction to learning classifier systems, it overviews the theoretical understanding we have today, the current research directions, the most interesting applications, and the open issues.