Artificial Intelligence Applications in Information and by Yacine Laalaoui, Nizar Bouguila

By Yacine Laalaoui, Nizar Bouguila

This ebook provides a variety of contemporary functions of man-made Intelligence in info and verbal exchange applied sciences reminiscent of seek and Optimization tools, computer studying, info illustration and Ontologies, and Multi-agent platforms. the most goal of this e-book is to assist details and communique applied sciences (ICT) practitioners in coping with successfully their structures utilizing AI instruments and techniques and to supply them with adequate man made Intelligence history to accommodate real-life difficulties.

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Solving scheduling problems in grid resource management using an evolutionary algorithm. In: On the Move to Meaningful Internet Systems 2006: CoopIS, DOA, GADA, and ODBASE. LNCS, vol. 4276, pp. 1252–1262. Springer (2006) 32. : Prediction of queue waiting times for metascheduling on parallel batch systems. In: Cirne, W. ) Job Scheduling Strategies for Parallel Processing. LNCS, vol. 8828. Springer (2015) 33. : QBETS: queue bounds estimation from time series. , Schwiegelshohn, U. ) Job Scheduling Strategies for Parallel Processing.

Job Scheduling Strategies for Parallel Processing. LNCS, vol. 1291, pp. 1–34. Springer (1997) 11. : Modeling user runtime estimates. , Schwiegelshohn, U. ) Job Scheduling Strategies for Parallel Processing. LNCS, vol. 3834, pp. 1–35. Springer (2005) 12. 1, Administrator’s Guide, Jan 2015. pbsworks. com 13. : Benefits of global grid computing for job scheduling. In: GRID’04: Proceedings of the 5th IEEE/ACM International Workshop on Grid Computing, pp. 374–379. IEEE (2004) 14. : Job fairness in non-preemptive job scheduling.

In ACO algorithms, artificial ants imitate this way of communication by using artificial pheromone, which is some numerical information saved on the states of the search space of the problem to solve. The first ACO algorithm known in the literature is the ant system AS. It has been proposed by Dorigo [1], and has various extended versions like the Max-Min AS called MMAS [2], the rank based version [3] (ASrank) and the Ant Colony System ACS [4]. The approach has gained a large reputation since it has solved with success many combinatorial optimization problems like the travelling salesman problem (TSP) [5], the quadratic assignment problem (QAP) [6], the vehicle routing problem (VRP) [7], the job shop scheduling problem (JSSP) [8] and the satisfiability problem (SAT) [9].

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