Multi-Agent Election-Based Hyper-Heuristics
Abstract
Hyper-heuristics are high-level methodologies responsible for automatically discover how to combine elements from a low-level heuristic set in order to solve optimization problems. Agents, in turn, are autonomous component responsible for watching an environment and perform some actions according to their perceptions. Thus, agent-based techniques seem suitable for the design of hyper-heuristics. This work presents an agent-based hyper-heuristic framework for choosing the best low-level heuristic. The proposed framework performs a cooperative voting procedure, considering a set of quality indicator voters, to define which multi-objective evolutionary algorithm (MOEA) should generate more new solutions along the execution.
Cite
Text
de Carvalho and Sichman. "Multi-Agent Election-Based Hyper-Heuristics." International Joint Conference on Artificial Intelligence, 2018. doi:10.24963/IJCAI.2018/833Markdown
[de Carvalho and Sichman. "Multi-Agent Election-Based Hyper-Heuristics." International Joint Conference on Artificial Intelligence, 2018.](https://mlanthology.org/ijcai/2018/decarvalho2018ijcai-multi/) doi:10.24963/IJCAI.2018/833BibTeX
@inproceedings{decarvalho2018ijcai-multi,
title = {{Multi-Agent Election-Based Hyper-Heuristics}},
author = {de Carvalho, Vinicius Renan and Sichman, Jaime Simão},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2018},
pages = {5779-5780},
doi = {10.24963/IJCAI.2018/833},
url = {https://mlanthology.org/ijcai/2018/decarvalho2018ijcai-multi/}
}