dynamic algorithm

dynamic algorithm

[dī¦nam·ik ′al·gə‚rith·əm]
(computer science)
An algorithm whose operation is, to some extent, unpredictable in advance, generally because it contains logical decisions that are made on the basis of quantities computed during the course of the algorithm. Also known as heuristic algorithm.
References in periodicals archive ?
The dynamic algorithm selection module was able to associate the created demands with the appropriate algorithms for the computation of paths.
To resolve it, various approaches have been developed in recent years, including hierarchical clustering, dynamic algorithm, and optimization.
The authors in [9] proposed another dynamic algorithm with the same name of DTXOP that TXOP is periodically updated for each AC according to the traffic conditions.
Sondergaard also gave examples such as, Amazon's recommendation algorithm that keeps customers engaged and buying; Netflix's dynamic algorithm -- built through crowdsourcing -- keeps people watching; and the Waze algorithm that directs thousands of independent cars on the road.
Second, a dynamic algorithm that recomputes optimal path in each ITA lifecycle call based on internal knowledge base via mechanism presented in the previous chapter.
In particular, the dynamic algorithm wastes more energy than that of the binary algorithm because the dynamic algorithm requires two control packets for estimating the optimal TPL, while one control packet is enough for the binary TPC algorithm.
The dynamic algorithm (28) converges to the unique Nash equilibrium if
This is achieved by broadcasting each change of the network topology to all nodes [18] and by using a centralized dynamic algorithm for shortest paths, as for example that in [12].
The dynamic algorithm of the two-axis force sensor can be obtained as shown in (10):
(2002) developed a link-based artificial neural network (ANN) and a stop-based ANN to predict bus arrival time where a dynamic algorithm was also presented to dynamically improve outputs.
The presentations discuss such topics as visualization of non-vectorial data using twin kernel embedding, dynamic algorithm selection using reinforcement learning, mining better technical trading strategies with genetic algorithms, finding the right features for instrument classification of classical music, quantification of intermarket influence based on global optimization and its application for stock market prediction, efficient fuzzy rules for classification, and computational quantification of trust updates, among others.
The dynamic algorithm also adjusts bandwidth allocation across the number of 802.11a, b or g clients carrying traffic to ensure seamless, optimal performance for every user.

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