Competitive Environments Evolve Better Solutions for Complex Tasks Export

@inproceedings{AnPo93, abstract = {In the typical genetic algorithm experiment, the fitness function is constructed to be independent of the contents of the population to provide a consistent objective measure. Such objectivity entails significant knowledge about the environment which suggests either the problem has previously been solved or other non-evolutionary techniques may be more efficient. Furthermore, for many complex tasks an independent fitness function is either impractical or impossible to provide. In this paper, we ...}, author = {Angeline, Peter J. and Pollack, Jordan B.}, booktitle = {Proceedings of the 5th International Conference on Genetic Algorithms (GA-93)}, citeulike-article-id = {1431444}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.56.9467}, keywords = {ea, evaluation}, pages = {264--270}, posted-at = {2008-04-21 15:11:24}, priority = {2}, title = {Competitive Environments Evolve Better Solutions for Complex Tasks}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.56.9467}, year = {1993} }

TY - CONF ID - AnPo93 L3 - citeulike-article-id:1431444 N2 - In the typical genetic algorithm experiment, the fitness function is constructed to be independent of the contents of the population to provide a consistent objective measure. Such objectivity entails significant knowledge about the environment which suggests either the problem has previously been solved or other non-evolutionary techniques may be more efficient. Furthermore, for many complex tasks an independent fitness function is either impractical or impossible to provide. In this paper, we ... EP - 270 TI - Competitive Environments Evolve Better Solutions for Complex Tasks T2 - Proceedings of the 5th International Conference on Genetic Algorithms (GA-93) SP - 264 KW - ea KW - evaluation AU - Angeline, Peter AU - Pollack, Jordan PY - 1993/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.56.9467 ER -