Applying Self-Organizing Feature Maps to the Control of Artificial Organisms in Maze Running Tasks

Variations on the now standard Kohonen feature map enable an ordering of the map state space by using only a limited subset of the complete input vector. Also it is possible to employ merely a local adaptation procedure to order the map, rather than having to rely on global variables and objectives. Such variations have been included as part of a Hybrid Learning System (HLS) which has arisen out of a genetic-based classifier system. In this paper a description of the modified feature map is given, this constituting the HLS´s long term memory, and results on the control of simple maxe running task are presented, thereby demonstrating the value of goal related feedback within the overall network.