Abstract
Indirect encoding is a promising area of research in machine learning/evolutionary computation, however, it is rarely able to achieve performance on par with state of the art directly encoded methods. One of the most important properties of indirect encoding is the ability to control exploration during learning by transforming random genotypic variation into an arbitrary distribution of phenotypic variation. This gives indirect encoding a capacity to learn to be adaptable in a way which is not possible for direct encoding. However, during normal objective based learning, there is no direct selection for adaptability, which results in not only a missed opportunity to improve the ability to learn, but often degrading it too. The recent meta learning algorithm MAML makes it possible to directly and efficiently optimize for the ability to adapt. This paper demonstrates that even when indirect encoding can be detrimental to performance in the case of normal learning, when selecting for the ability to adapt, indirect encoding can outperform direct encoding in a fair comparison. The indirect encoding technique Hypernetwork was used on the task of few shot image classification on the Omniglot dataset. The results show the importance of directly optimizing for adaptability in realizing the powerful potential of indirect encoding.
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Acknowledgement
This work was supported by the EPSRC Centre for Doctoral Training in Intelligent Games & Game Intelligence (IGGI) [EP/L015846/1] and the Digital Creativity Labs funded by EPSRC/AHRC/Innovate UK [EP/M023265/1]. This work was partially supported by Society for the Promotion of Evolutionary Computation in Europe and its Surroundings (SPECIES).
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Katona, A., Lourenço, N., Machado, P., Franks, D.W., Walker, J.A. (2021). Utilizing the Untapped Potential of Indirect Encoding for Neural Networks with Meta Learning. In: Castillo, P.A., Jiménez Laredo, J.L. (eds) Applications of Evolutionary Computation. EvoApplications 2021. Lecture Notes in Computer Science(), vol 12694. Springer, Cham. https://doi.org/10.1007/978-3-030-72699-7_34
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