Abstract
Using Genetic Programming difficult optimisation
problems can be solved, even if the candidate solutions
are complex objects. In such cases, it is a costly
procedure to correct or replace the invalid individuals
that may appear during the evolutionary process.
Instead of such post-processing, context-free grammars
can be used to describe the syntax of valid solutions,
and the algorithm can be modified to work on derivation
trees, such that it does not generate invalid
individuals. Although tree operators have the advantage
of good parameterizability, it is not trivial to
construct them correctly and efficiently. An existing
method for derivation tree evolution and its extension
towards attributed derivation trees are discussed. As
the result of this extension the operators are not only
faster but they are easy to parameterise, moreover the
algorithm is better guided, thus it can converge
faster.
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