Kuan-Wu Su
ABSTRACT
In this study, a neuroevolution strategy using Multi-Agent Incorporated Hierarchical Ensemble Model (MAIHEM) inspired by human incorporated company structure is proposed. It utilizes the hierarchical structure to ensemble modules of entities into firms to preserve complex structure at lower level, and at higher level incorporates firms into departments to facilitate multiple objectives. The corporate level structure from the top guides and reviews their overall performance. The ensemble structure not only compete within their own ranks, but also cooperate and swap/merger underlying units for fast adaptation without compromising their existing structures. The preliminary result with multi-constrained music melody generation shows this strategy can not only solve complex multi-objective tasks steadily but also preserve diversity in the population.
- F. P. Such, V. Madhavan, E. Conti, J. Lehman, K. O. Stanley, J. Clune. 2017. Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning. arXiv preprint arXiv:1712.06567.Google Scholar
- M. Lin, Q. Chen, S. Yan. 2013. Network in network. arXiv preprint arXiv:1312.4400.Google Scholar
- K.-W. Su. 2012. Study on Retrieving Subjective Knowledge from Music Using Adaptive Cloud-Based Structure. Master's thesis. National Taiwan University of Science and Technology (NTUST), Taipei, Taiwan.Google Scholar
- J. Lehman, K. O. Stanley. 2011. Abandoning objectives: Evolution through the search for novelty alone. Evolutionary computation, 19(2), 189--223. Google ScholarDigital Library
- MAIHEM melody generation samples. 2018. https://goo.gl/GmGVLq Accessed: 2018- 02- 06.Google Scholar
Index Terms
- A neuroevolution strategy using multi-agent incorporated hierarchical ensemble model
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