Estimation of Distribution using Population Queue based Variational Autoencoders
| Autor: | Robin Gras, Sourodeep Bhattacharjee |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
education.field_of_study
021103 operations research Population 0211 other engineering and technologies Probabilistic logic Statistical model 02 engineering and technology Autoencoder Generative model Estimation of distribution algorithm 0202 electrical engineering electronic engineering information engineering Benchmark (computing) 020201 artificial intelligence & image processing education Queue Algorithm |
| Zdroj: | CEC |
| DOI: | 10.1109/cec.2019.8790077 |
| Popis: | We present a new Estimation of Distribution algorithms (EDA) based on two novel Variational Autoencoders generative model building algorithms. The first method, Variational Autoencoder with Population Queue (VAE-EDA-Q), employs a queue of historical populations, which is updated at each iteration of EDA in order to smooth the data generation process. The second method uses Adaptive Variance Scaling (AVS) with VAE-EDA-Q to dynamically update the variance at which the probabilistic model is sampled. The results obtained prove our methods to be significantly more computationally efficient than state-of-the-art algorithms and perform significantly less number of fitness evaluations when tested on benchmark problems such as Trap-k and NK Landscapes. Moreover, we report results of applying our approach successfully to highly complex problems such as Trap 11, Trap 13, and NK Landscapes with neighborhood size K = 8 and K = 10. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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