A Hybrid Simplex Search for Global Optimization with Representation Formula and Genetic Algorithm

Zidani, Hafid; Ellaia, Rachid; Cursi, Eduardo Souza de

doi:10.1007/978-3-030-21803-4_1

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…Nowadays, a great number of hybrid algorithms taking advantage of both gradient descent and evolution strategy have shown their effectiveness over optimization problems [17,18] such as function optimization. Other population-based algorithms [19] also show potential to work together with gradient-based methods to achieve better performance.…”

Section: Introductionmentioning

confidence: 99%

Gumbel-softmax-based optimization: a simple general framework for optimization problems on graphs

Liu

Lin

et al. 2021

Comput Soc Netw

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In computer science, there exist a large number of optimization problems defined on graphs, that is to find a best node state configuration or a network structure, such that the designed objective function is optimized under some constraints. However, these problems are notorious for their hardness to solve, because most of them are NP-hard or NP-complete. Although traditional general methods such as simulated annealing (SA), genetic algorithms (GA), and so forth have been devised to these hard problems, their accuracy and time consumption are not satisfying in practice. In this work, we proposed a simple, fast, and general algorithm framework based on advanced automatic differentiation technique empowered by deep learning frameworks. By introducing Gumbel-softmax technique, we can optimize the objective function directly by gradient descent algorithm regardless of the discrete nature of variables. We also introduce evolution strategy to parallel version of our algorithm. We test our algorithm on four representative optimization problems on graph including modularity optimization from network science, Sherrington–Kirkpatrick (SK) model from statistical physics, maximum independent set (MIS) and minimum vertex cover (MVC) problem from combinatorial optimization on graph, and Influence Maximization problem from computational social science. High-quality solutions can be obtained with much less time-consuming compared to the traditional approaches.

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Section: Introductionmentioning

confidence: 99%

Gumbel-softmax-based optimization: a simple general framework for optimization problems on graphs

Liu

Lin

et al. 2021

Comput Soc Netw

View full text Add to dashboard Cite

show abstract

“…Nowadays, a great number of hybrid algorithms taking advantage of both gradient descent and evolution strategy have shown their effectiveness over optimization problems [16,17] such as function optimization. Other population based algorithms [18] also show potential to work together with gradient based methods to achieve better performance.…”

Section: Introductionmentioning

confidence: 99%

Gumbel-softmax-based Optimization: A Simple General Framework for Optimization Problems on Graphs

Liu

Lin

et al. 2020

Preprint

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In computer science, there exist a large number of optimization problems defined on graphs, that is to find a best node state configuration or a network structure such that the designed objective function is optimized under some constraints. However, these problems are notorious for their hardness to solve because most of them are NP-hard or NP-complete. Although traditional general methods such as simulated annealing (SA), genetic algorithms (GA) and so forth have been devised to these hard problems, their accuracy and time consumption are not satisfying in practice. In this work, we proposed a simple, fast, and general algorithm framework based on advanced automatic differentiation technique empowered by deep learning frameworks. By introducing Gumbel-softmax technique, we can optimize the objective function directly by gradient descent algorithm regardless of the discrete nature of variables. We also introduce evolution strategy to parallel version of our algorithm. We test our algorithm on three representative optimization problems on graph including modularity optimization from network science, Sherrington-Kirkpatrick (SK) model from statistical physics, maximum independent set (MIS) and minimum vertex cover (MVC) problem from combinatorial optimization on graph. High-quality solutions can be obtained with much less time consuming compared to traditional approaches.

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Morphological filters for global optimization

Ayouche

Ellaia

Ennatiki

2020

2020 5th International Conference on Logistics Operations Management (GOL)

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A Hybrid Simplex Search for Global Optimization with Representation Formula and Genetic Algorithm

Cited by 3 publications

References 4 publications

Gumbel-softmax-based optimization: a simple general framework for optimization problems on graphs

Gumbel-softmax-based optimization: a simple general framework for optimization problems on graphs

Gumbel-softmax-based Optimization: A Simple General Framework for Optimization Problems on Graphs

Morphological filters for global optimization

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