Multi-Objective Learning via Genetic Algorithms
Proceedings of the Ninth International Joint Conference on Artificial Intelligence. Learning and Acquisition. Pages 593-594.
Abstract
Genetic algorithms (GAs) are powerful, general purpose adaptive search techniques which have been used successfully in a variety of learning systems. In the standard formulation, GAs maintain a set of alternative knowledge structures for the task to be learned, and improved knowledge structures are formed through a combination of competition and knowledge sharing among the alternative knowledge structures. In this paper, we extend the GA paradigm by allowing multidimensional feedback concerning the performance of the alternative structures. The modified GA is shown to solve a multiclass pattern discrimination task which could not be solved by the unmodified GA.
@inproceedings{ijcai1985p113,
title = {Multi-Objective Learning via Genetic Algorithms},
author = {Schaffer, J. David and Grefenstette, John J.},
booktitle = {Proceedings of the Ninth International Joint Conference on
Artificial Intelligence, {IJCAI-85}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Aravind Joshi},
pages = {593--594},
year = {1985},
note = {Learning and Acquisition},
url = {https://ijcai.org/proceedings/1985/113},
}
TY - CPAPER TI - Multi-Objective Learning via Genetic Algorithms AU - Schaffer, J. David AU - Grefenstette, John J. T2 - Proceedings of the Ninth International Joint Conference on Artificial Intelligence, IJCAI-85 PB - International Joint Conferences on Artificial Intelligence PY - 1985 SP - 593 EP - 594 UR - https://ijcai.org/proceedings/1985/113 ER -Copied
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