Titlebook: Learning and Intelligent Optimization; 15th International C Dimitris E. Simos,Panos M. Pardalos,Ilias S. Kotsi Conference proceedings 2021

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0302-9743 n June 2021. ..The 30 full papers presented have been carefully reviewed and selected from 35 submissions. LION deals with designing and engineering ways of "learning" about the performance of different techniques, and ways of using past experience about the algorithm behavior to improve performance

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,Reparameterization of?Computational Chemistry Force Fields Using GloMPO (Globally Managed Parallel e to poor minima. By managing an optimization task, GloMPO: 1) improves the efficiency with which an iteration budget is used; 2) provides better answers 60% to 80% of the time as compared to traditional approaches; and 3) is often able to identify several independent and degenerate minima.

爆米花

,An Optimization for?Convolutional Network Layers Using the?Viola-Jones Framework and?Ternary Weightby replacing convolutional filters with a set of custom ones inspired by the framework. This reduces the number of operations needed for computing feature values with negligible effects on overall accuracy, allowing for a more optimized network.

Amylase

,Graph Diffusion & PCA Framework for?Semi-supervised Learning,s node classification by enriching the local graph structure by node covariance. We demonstrate the performance of GDPCA in experiments on citation networks and images, and we show that GDPCA compares favourably with the best state-of-the-art algorithms and has significantly lower computational complexity.

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,Exact Counting and?Sampling of?Optima for?the?Knapsack Problem,number of optima develops for classical random benchmark instances dependent on their generator parameters. We find that the number of global optima can increase exponentially for practically relevant classes of instances with correlated weights and profits which poses a justification for the considered exact counting problem.

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,Set Team Orienteering Problem with?Time Windows,s. We propose an adaptive large neighborhood search algorithm to solve newly introduced benchmark instances. The preliminary results show the capability of the proposed algorithm to obtain good solutions within reasonable computational times compared to commercial solver CPLEX.

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