G2.3 Genetic information learning

Supervised learning in attribute-based spaces is one of the most popular machine learning problems studied and, consequently, has attracted considerable attention from the evolutionary computation community. The problem studied here is typical—determining optimal symbolic descriptions for a concept, for which positive and negative examples are provided along with an appropriate language. Key difficulties stem from such concept descriptions being sets of elementary descriptions. The approach presented here uses a variable-length representation—each chromosome represents a complete set of these elementary elements. Another difficulty lies in the gap between the abstract variablelength phenotype and the often used binary genotype. This problem is avoided by defining the evolutionary search at the phenotype level. Finally, most other evolutionary approaches suffer from high time complexity. The approach presented in this case study alleviates this problem by utilizing problem specific search operators and heuristics and by precompiling data to facilitate faster evaluations.