A DGA is a genetic algorithm with novel features: relational schemata. These structures allow a more natural expression of relations existing between loci. Indeed, schemata in standard genetic algorithms can only specify values for each locus. Relational schemata are based on the notion of duality: a schema can be represented by two strings. The intent of this paper is to show the superiority of DGAs over conventional genetic algorithms in two general areas: efficiency and reliability. Thus, we show with theoretical and experimental results, that our algorithm is faster and perform consistently. The application chosen for test DGAs is the optimization of an extension of Royal Road functions we call relational landscapes.
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