Abstract
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 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, which constitutes the HLSs long term memory, and results on the control of a simple maze running task are presented, thereby demonstrating the value of goal related feedback within the overall network.
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