Titlebook: Artificial Evolution; 13th International C Evelyne Lutton,Pierrick Legrand,Marc Schoenauer Conference proceedings 2018 Springer Internation

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显微镜

On the Design of a Master-Worker Adaptive Algorithm Selection Framework,ently proposed benchmark family for adaptive algorithm selection, reveals the accuracy of the proposed framework while providing new insights on the performance of distributed adaptive optimization algorithms.

Awning

Comparison of Acceptance Criteria in Randomized Local Searches, the quadratic assignment problem and a permutation flowshop problem. Our experimental results show that, while results may differ depending on the specific problem, the Metropolis condition and the late acceptance hill climbing rule are among the choices that obtain the best results.

清晰

Basic, Dual, Adaptive, and Directed Mutation Operators in the Fly Algorithm,progressively reduced to refine the results. Another approach is the use of adaptive operators. However, very little research on adaptive operators in Fly algorithm has been conducted. We address this deficiency and propose 4 different fully adaptive mutation operators in the Fly algorithm: Basic Mu

非实体

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Offline Learning for Selection Hyper-heuristics with Elman Networks,rning and generalisation with 99% confidence and produces better results than the training sequences in many cases. When the network was trained using an inter-domain training set, the Elman network did not exhibit generalisation indicating that inter-domain generalisation is a harder problem and th

Callus

On the Design of a Master-Worker Adaptive Algorithm Selection Framework,iven portfolio a set of operators to be executed in parallel, and consequently (ii) take full advantage of the compute power offered by the underlying distributed environment. In fact, it is still an open issue to design online distributed strategies that are able to optimally assign operators to pa

伸展

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