Improving grammar-based evolutionary algorithms via attributed derivation trees

Abstract

Using Genetic Programming difficult optimization 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 postprocessing, 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. In this paper an already 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 parameterize, moreover the algorithm is better guided, thus it can converge faster. © Springer-Verlag 2004.