Improved training via incremental learning
Proceedings of the sixth international workshop on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
HGA: a hardware-based genetic algorithm
FPGA '95 Proceedings of the 1995 ACM third international symposium on Field-programmable gate arrays
Decision Tree Induction Based on Efficient Tree Restructuring
Machine Learning
Genetic algorithm accelerator GAA-II
ASP-DAC '00 Proceedings of the 2000 Asia and South Pacific Design Automation Conference
Artificial Intelligence Review - Special issue on lazy learning
A hybrid decision tree/genetic algorithm method for data mining
Information Sciences: an International Journal - Special issue: Soft computing data mining
Adaptive evolutionary planner/navigator for mobile robots
IEEE Transactions on Evolutionary Computation
Fitness sharing and niching methods revisited
IEEE Transactions on Evolutionary Computation
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This paper describes on-going research work into real-time machine learning using embedded software and reconfigurable hardware. The main focus of the work is to develop real-time incremental learning methods particularly targeted at demonstration in mobile robot environments. Three main areas are described. The first represents reactive robot navigation knowledge using a novel frequency table technique whose memory requirement is known a priori. The second area investigates a Genetic Algorithm (GA) method that combines planning and reactive approaches to allow navigation to proceed even in the face of time constraints. In the third area we are developing novel hardware-based machine learning systems suitable for implementation in reconfigurable platforms.