Adopting the blackboard architecture from the area of Artificial Intelligence, a novel kind of optimizer enabling two desirable ideas will be proposed. Firstly, using such a well-structured approach backpropagation of the optimized queries allows an evolutionary improvement of (crucial) parts of the optimizer. Secondly, the A search strategy can be applied to harmonize two contrary properties: Alternatives are generated whenever necessary, and straight-forward optimizing is performed whenever possible, however. The generic framework for realizing a blackboard optimizer is proposed first. Then, in order to demonstrate the viability of the new approach, a simple example optimizer is presented. It can be viewed as an incarnation of the generic framework.
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