Controlling Sequential Hybrid Evolutionary Algorithm by Q-Learning [Research …
Many state-of-the-art evolutionary algorithms (EAs) can be categorized as sequential hybrid EAs, in which various EAs are sequentially executed. The timing to switch from one EA to another is criti…
Digital Subcarrier Multiplexing: Enabling Software-Configurable Optical Networks
The various topologies, traffic patterns and cost targets of optical networks have prevented the deployment of end-to-end solutions across multi-domains, and the optimization of the network as a wh…
Automated Configuration of Genetic Algorithms by Tuning for Anytime Performance
Finding the best configuration of algorithms’ hyperparameters for a given optimization problem is an important task in evolutionary computation. We compare in this work the results of four differ…
Multitask Shape Optimization Using a 3-D Point Cloud Autoencoder as Unified R…
The choice of design representations, as of search operators, is central to the performance of evolutionary optimization algorithms, in particular, for multitask problems. The multitask approach pu…
Learning Adaptive Differential Evolution Algorithm From Optimization Experien…
Differential evolution is one of the most prestigious population-based stochastic optimization algorithm for black-box problems. The performance of a differential evolution algorithm depends highly…
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