Abstract
Many engineering problems may be described as a search
for one near optimal description amongst many
possibilities, given certain constraints. Search
techniques such as genetic programming, seem
appropriate to represent many problems. The paper
describes a grammatically based learning technique
based upon the genetic programming paradigm, that
allows declarative biasing and modifies the bias as the
evolution proceeds. The use of bias allows complex
problems to be represented and searched efficiently
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