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Fuzzy Aggregated Topology Evolution for Cognitive Multi-tasks
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12559_2020_9807_ReferencePDF.pdf
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806.81 KB
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Adobe PDF
Checksum (MD5)
3c997514be2a156a83da0a4d8b0029b0
Author(s)
Chaturvedi, Iti
Su, Chit L
Welsch, Roy E
Date Issued
January 5, 2021
Publisher
Springer US
Version
Author's final manuscript
Abstract
Abstract
Evolutionary optimization aims to tune the hyper-parameters during learning in a computationally fast manner. For optimization of multi-task problems, evolution is done by creating a unified search space with a dimensionality that can include all the tasks. Multi-task evolution is achieved via selective imitation where two individuals with the same type of skill are encouraged to crossover. Due to the relatedness of the tasks, the resulting offspring may have a skill for a different task. In this way, we can simultaneously evolve a population where different individuals excel in different tasks. In this paper, we consider a type of evolution called Genetic Programming (GP) where the population of genes have a tree-like structure and can be of different lengths and hence can naturally represent multiple tasks. We apply the model to multi-task neuroevolution that aims to determine the optimal hyper-parameters of a neural network such as number of nodes, learning rate, and number of training epochs using evolution. Here each gene is encoded with the hyper parameters for a single neural network. Previously, optimization was done by enabling or disabling individual connections between neurons during evolution. This method is extremely slow and does not generalize well to new neural architectures such as Seq2Seq. To overcome this limitation, we follow a modular approach where each sub-tree in a GP can be a sub-neural architecture that is preserved during crossover across multiple tasks. Lastly, in order to leverage on the inter-task covariance for faster evolutionary search, we project the features from both tasks to common space using fuzzy membership functions. The proposed model is used to determine the optimal topology of a feed-forward neural network for classification of emotions in physiological heart signals and also a Seq2seq chatbot that can converse with kindergarten children. We can outperform baselines by over 10% in accuracy.
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1007/s12559-020-09807-4
