This paper presents the study conducted on the optimization algorithm called KGCS (Knowledge Gradient Cuckoo Search) using benchmark functions. The algorithm is highly effective due to the combination of the specific characteristics of the Cuckoo Search algorithm and the ones of the Knowledge Gradient policy. The paper also presents the benchmark mathematical functions usually used to test optimization algorithms such as Rosenbrock, Griewank or Ackley’s function. The results of the conducted tests are compared to the results obtained using the unenhanced evolutionary algorithms in order to prove efficiency of the KGCS algorithm.  

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Key words: evolutionary algorithm, optimization, benchmark functions, Cuckoo Search algorithm.


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