A new hillclimber for classifier systems

Multistate artificial environments such as mazes represent a class of tasks that can be solved by many different multistep methods. When different rewards are available in different places of the maze, a problem solver is required to evaluate different positions effectively and remembers the best one. A new hillclimbing strategy for the Michigan style classifier system is suggested which is able to find the shortest path and discarding suboptimal solutions. Knowledge reuse is also shown to be possible.