抄録
We present the result of our work on the role of genetic representation in facilitating quick design of efficiently running offline learning via genetic programming (GP). An approach of using the widely adopted DOM/XML standard for representation of genetic programs and off-the-shelf DOM-parsers with build-in API for manipulating them is proposed. The approach features significant reduction of time consumption of usually slow software engineering of GP and offers a generic way to facilitate the reduction of computational effort by limitation of search space of genetic programming via handling of only semantically correct genetic programs. The concept is accomplished through strongly typed genetic programming (STGP), in which the use of W3C-recommended standard XML schema is proposed as a generic way to represent and impose the grammar rules in STGP. The ideas laid in the foundation of the proposed approach are verified on the implementation of GP for evolving social behavior of agents in predator prey pursuit problem.
