USING SELF-ORGANIZING FEATURE MAPS FOR THE CONTROL OF ARTIFICIAL ORGANISMS

Variations on the standard Kohonen feature map can 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 varia bles and objectives. Such variations have been included as part of a h ybrid learning system (HLS) which has arisen out of a genetic-based cl assifier system. In this paper a description of the modified feature m ap is given, which constitutes the HLSs long term memory, and results on the control of a simple maze running task are presented, thereby de monstrating the value of goal related feedback within the overall netw ork.