Abstract
This article presents the results of our work on the role of genetic representation in facilitating the quick design of efficiently running offline learning via genetic programming (GP). An approach using the widely adopted document object model/extensible mark-up language (DOM/XML) standard for the representation of genetic programs, and off-the-shelf DOM-parsers with built-in application programming interface (API) for manipulating them is proposed. This approach means a significant reduction in time in the usually slow software engineering of GP, and offers a generic way to facilitate the reduction of computational effort by limiting the search space of genetic programming by handling 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 by the implementation of GP in the evolving social behavior of agents in predator–prey pursuit problems.
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This work was presented in part at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24–26, 2003
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Tanev, I. DOM/XML-based portable genetic representation of the morphology, behavior and communication abilities of evolvable agents. Artif Life Robotics 8, 52–56 (2004). https://doi.org/10.1007/s10015-004-0288-6
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DOI: https://doi.org/10.1007/s10015-004-0288-6