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2 2007

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LCS Approach to Tasks Scheduling Problem in the Two Processor System

Wasielewska K., Seredyński F.

Studia Informatica : systems and information technology

2007

Vol. 2(9)

29--39

In this paper we propose an approach to solve multiprocessor scheduling problem with use of rule-based learning machine - Learning Classifier System (LCS). LCS combines reinforcement learning and evolutionary computing to produce adaptive systems. We interpret the multiprocessor scheduling problem as multi-step problem, where a feedback is given after some number steps. We show that LCS is able to solve scheduling tasks of a parallel program in the two processor system.

Real–valued GCS classifier system

Cielecki Ł., Unold O.

International Journal of Applied Mathematics and Computer Science

2007

Vol. 17, no 4

539-547

Learning Classifier Systems (LCSs) have gained increasing interest in the genetic and evolutionary computation literature. Many real-world problems are not conveniently expressed using the ternary representation typically used by LCSs and for such problems an interval-based representation is preferable. A new model of LCSs is introduced to classify realvalued data. The approach applies the continous-valued context-free grammar-based system GCS. In order to handle data effectively, the terminal rules were replaced by the so-called environment probing rules. The rGCS model was tested on the checkerboard problem.