Neurocomputing: foundations of research
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Artificial neural network implementation on a single FPGA of a pipelined on-line backpropagation
ISSS '00 Proceedings of the 13th international symposium on System synthesis
Robot Shaping: An Experiment in Behavior Engineering
Robot Shaping: An Experiment in Behavior Engineering
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
A Hardware Implementation of a Genetic Programming System Using FPGAs and Handel-C
Genetic Programming and Evolvable Machines
FPGA and CPLD Architectures: A Tutorial
IEEE Design & Test
Learning Classifier Systems, From Foundations to Applications
Learning Classifier Systems, From Foundations to Applications
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
A Preliminary Investigation of Modified XCS as a Generic Data Mining Tool
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
XCS and GALE: A Comparative Study of Two Learning Classifier Systems on Data Mining
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
A Hardware Genetic Algorithm for the Travelling Salesman Problem on SPLASH 2
FPL '95 Proceedings of the 5th International Workshop on Field-Programmable Logic and Applications
Applications of Learning Classifier Systems
Applications of Learning Classifier Systems
Classifier fitness based on accuracy
Evolutionary Computation
Toward a theory of generalization and learning in XCS
IEEE Transactions on Evolutionary Computation
| Hi-index | 0.00 |
The paper presents the first results of the prototype implementation of the eXtended learning Classifier System (XCS) in hardware and precisely on Field Programmable Gate Arrays. For this purpose we introduce a version of the XCS classifier system completely based on integer arithmetic, that we name XCSi, instead of the usual floating point one, to exploit the peculiarities and overcome the limitations of the hardware platform. We present an analysis of XCSi performance and the guidelines for a hardware implementation, showing that, although there is a dramatic reduction of available precision, the integer version of XCS can reach optimal performance in all problems considered, though it often converges more slowly than the original floating point version. Guidelines for a hardware implementation are provided, by analyzing how XCSi functional components can be designed on an FPGA.