An improved genetic algorithm for the integrated satellite imaging and data transmission scheduling problemJiawei Zhang aLining Xing b
Ndiritu, JG, Daniell, TM (1999) An improved genetic algorithm for continuous and mixed discrete-continuous optimization. Eng Optim 31: pp. 589-614An improved genetic algorithm for continuous and mixed discretecontinuous optimization - Ndiritu, Daniell - 1999...
Yan, X., W. Luo, W.Li, W.Chen, C. Zhang, and H. Liu, 2013, An Improved Genetic Algorithm and Its Application in Classification. IJCSI International Journal of Computer Science Issues, Vol. 10, Issue 1, No 1, January 2013, pp. 337-346....
It is observed that, the efficiency of GA in solving a flowshop problem can be improved significantly by tailoring the various GA operators to suit the structure of the problem. In this paper, an effective Improved Genetic Algorithm (IGA) for flowshop scheduling, incorporating multi-crossover ...
Improved genetic algorithm (IGA)ElectrochemicalE0(V)A(V)in(mA/cm2)i0(mA/cm2)r(kΩ/cm2)m(V)n(cm2/mA) Range Set(0, 1.2)(0, 1)(0, 10)(0, 10)(0, 1)(0, 1)(0, 1) E.P.1.09200.02962.09068.89900.00350.00880.02194 AE0.3373 ...
Please cite this article in press as: L. De Giovanni, F. Pezzella, An Improved Genetic Algorithmfor the Distributed and Flexible Job-shop ..., European Journal of Operational Research (2009), doi:10.1016/j.ejor.2009.01.008 –determining the most suitable FMU (cell or factory) for each job...
The genetic algorithm (GA) is one of the most useful algorithms for solving this problem. In this paper a conventional GA is compared with an improved hybrid GA in solving TSP. The improved or hybrid GA consist of conventional GA and two local optimization strategies. The first strategy is ...
Genetic algorithm is a heuristic population-based search method that incorporates three primary operators: crossover, mutation and selection. Selection operator plays a crucial role in finding optimal solution for constrained optimization problems. In this paper, an improved genetic algorithm (IGA) based...
In this section, we present the performance (i.e., the optimality) results of our proposed Genetic Algorithm (GA) by comparing it with benchmarking algorithms over a cellular network (5G) with Edge Computing (EC) capabilities. This section is structured as follows. First, Section 6.3 presents...
Therefore, this article combines the improved niche genetic algorithm with K-means algorithm to produce a new improved algorithm named an improved niche genetic clustering algorithm. It is verified that the algorithm is valid in improving the quality of clustering analysis....