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Titlebook: Intelligent Problem Solving. Methodologies and Approaches; 13th International C Rasiah Logananthara,Günther Palm,Moonis Ali Conference proc

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樓主: ominous
11#
發(fā)表于 2025-3-23 13:45:42 | 只看該作者
12#
發(fā)表于 2025-3-23 14:31:17 | 只看該作者
Conference proceedings 2000he mechanisms that the human brain employs to solve problems are not yet completely known, we do have good insight into the functional processing performed by the human mind. On the basis of the understanding of these natural processes, scientists in the field of applied intelligence have developed
13#
發(fā)表于 2025-3-23 20:49:55 | 只看該作者
14#
發(fā)表于 2025-3-24 00:04:04 | 只看該作者
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發(fā)表于 2025-3-24 03:06:53 | 只看該作者
16#
發(fā)表于 2025-3-24 09:58:00 | 只看該作者
17#
發(fā)表于 2025-3-24 13:05:31 | 只看該作者
A Simulation-Based Procedure for Expert System Evaluationrocedure to evaluate the developed expert system by using a simulation-based approach is presented in details. It is also concluded that the simulation-based procedure is feasible and effective for evaluating expert systems in a number of different domains.
18#
發(fā)表于 2025-3-24 18:28:36 | 只看該作者
Information Management and Process Improvement Using Data Mining Techniquesthe possible enhancement of these consortium relationships and it also investigates the use of data mining techniques with the potential to advance information management and process improvement within the manufacturing environment concerned.
19#
發(fā)表于 2025-3-24 19:35:04 | 只看該作者
A Brokering Algorithm for Cost & QoS-Based Winner Determination in Combinatorial Auctionsn be solved by the available services. In the second stage, a genetic algorithm with heuristics is used to find the optimal combination of service providers to provide the services identified. We show through various experiments that the genetic algorithm finds optimal solutions quicker than a modified depth-first search algorithm.
20#
發(fā)表于 2025-3-25 02:02:38 | 只看該作者
An Overview of a Synergetic Combination of Local Search with Evolutionary Learning to Solve Optimizay the technique to optimally schedule the robot arm of an automated retrieval system. Obtaining optimal solutions to such scheduling problems is computationally intractable, but experimental results show our technique produces better solutions than those found by genetic algorithm with random key encoding.
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