Analysis of a batch strategy for a Master-Worker adaptive selection algorithm framework

Abstract : We look into the design of a parallel adaptive algorithm embedded in a master-slave scheme. The adaptive algorithm under study selects online and in parallel for each slave-node one algorithm from a portfolio. Indeed, many open questions still arise when designing an online distributed strategy that attributes optimally algorithms to distribute resources. We suggest to analyze the relevance of existing sequential adaptive strategies related to multi-armed bandits to the master-slave distributed framework. In particular, the comprehensive experimental study focuses on the gain of computing power, the adaptive ability of selection strategies, and the communication cost of the parallel system. In fact, we propose an adaptive batch mode in which a sequence of algorithms is submitted to each slave computing node to face a possibly high communication cost.
Type de document :
Communication dans un congrès
IJCCI 2017 - 9th International Joint Conference on Computational Intelligence, Nov 2017, Madère, Portugal
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https://hal.archives-ouvertes.fr/hal-01643337
Contributeur : Christopher Jankee <>
Soumis le : mardi 21 novembre 2017 - 13:10:07
Dernière modification le : mardi 5 février 2019 - 13:50:02

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  • HAL Id : hal-01643337, version 1

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Christopher Jankee, Sébastien Verel, Cyril Fonlupt, Bilel Derbel. Analysis of a batch strategy for a Master-Worker adaptive selection algorithm framework. IJCCI 2017 - 9th International Joint Conference on Computational Intelligence, Nov 2017, Madère, Portugal. 〈hal-01643337〉

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