Lobo, F., Lima, C., Michalewicz, Z.: Parameter setting in evolutionary algorithms. Springer Publishing Company, Incorporated, Heidelberg (2007) MATHF. G. Lobo , C. F. Lima and Z. E. Michalewicz Parameter Setting in EvolutionaryAlgorithms (Studies in Computational Intelligence) , vol....
we look into ways to improve the conventional BO with the help of evolutionary algorithms. We have used evolutionary algorithms, taking into account their success in solving optimization problems and major advantages like
We present and evaluate a method for estimating the relevance and calibrating the values of parameters of an evolutionary algorithm. The method provides an information theoretic measure on how sensitive a parameter is to the choice of its value. This can be used to estimate the relevance of param...
The performance of APSDE is validated under four sets of benchmark problem suites from the Institute of Electrical and Electronics Engineers (IEEE) Congress on Evolutionary Computation (CEC), and compared with state-of-the-art optimization algorithms. The results show that the proposed algorithm has...
The results showed that jDE is better than, or at least comparable to, the standard DE algorithm (FADE) algorithm and other evolutionary algorithms from the literature when considering the quality of the solutions obtained. In the same context, Omran et al [19] proposed a Self-adaptive ...
algorithms8,9,10and evolutionary algorithms (EAs) such as the shuffled complex evolution method (SCE-UA)11have been introduced for calibration. For example, nearly all models for the rainfall-runoff process12,13and for ecosystem dynamics14involve unobservable parameters that require calibration. More...
Parameters setting of PID controller based on adaptive immune evolutionary algorithms基于免疫进化算法的PID参数整定 A New Adaptive Immune Evolutionary Algorithm(NAIEA) was proposed in this paper according to regulatory mechanism of the immune network in biological immune... HE Hong,QIAN Feng,何宏,......
In this paper, we show that sequential parameter optimization (SPO), a method that was designed for (offline) parameter tuning, can be successfully used as a controller for multistart approaches of evolutionary algorithms (EA). We demonstrate this by replacing the restart heuristic of the IPOP-CM...