Titlebook: Cuckoo Search and Firefly Algorithm; Theory and Applicati Xin-She Yang Book 2014 Springer International Publishing Switzerland 2014 Computa

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The Solomon R. Guggenheim Foundationing Graphical Processing Units. We also evaluate algorithm efficiency against a geometric version of the Genetic Programming algorithm with tournament selection. We present some rendering techniques for visualising the program problem space and therefore to aid in characterising algorithm behaviour.

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Introduction: A Business Point of Viewimple and complex trusses that are widely reported in the literature as benchmark examples solved with several non-heuristic and heuristic algorithms. The results show that the algorithm outperforms the deterministic algorithms in accuracy, particularly using the Parallel Synchronous FA version.

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Cuckoo Search and Firefly Algorithm: Overview and Analysis,out why they are efficient. We also discuss the essence of algorithms and its link to self-organizing systems. In addition, we also discuss important issues such as parameter tuning and parameter control, and provide some topics for further research.

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How to Generate the Input Current for Exciting a Spiking Neural Model Using the Cuckoo Search Algor we describe how the Cuckoo Search algorithm can be used to train a spiking neuron and determine the best way to compute the input current for solving a pattern classification task. The accuracy of the methodology is tested using several pattern recognition problems.

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