The main contribution of the paper is proposing and evaluating, through the computational experiment, an agent-based population learning algorithm generating a representative training dataset of the required size. The...
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ISBN:
(纸本)9783642219993;9783642220005
The main contribution of the paper is proposing and evaluating, through the computational experiment, an agent-based population learning algorithm generating a representative training dataset of the required size. The proposed approach is based on the assumption that prototypes are selected from clusters. Thus, the number of clusters produced has a direct influence on the size of the reduced dataset. Agents within an A-Team execute various local search procedures and cooperate to find-out a solution to the instance reduction problem aiming at obtaining a compact representation of the dataset. Computational experiment has confirmed that the proposed algorithm is competitive to other approaches.
The paper investigates a possibility of combining the population learning algorithm and the A-Team concept with a view to increase quality of results and efficiency of computations. To implement the idea a middleware ...
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ISBN:
(纸本)9783540748175
The paper investigates a possibility of combining the population learning algorithm and the A-Team concept with a view to increase quality of results and efficiency of computations. To implement the idea a middleware environment called JABAT is used. The proposed approach is validated experimentally using benchmark datasets containing instances of the two well-known combinatorial optimization problems: flow shop and job shop scheduling.
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