But, as we have seen, the programs need not be trees, and similarly the search algorithm does not have to be a genetic algorithm. Other techniques include: local search, Simulated Annealing [221, 222], Differential Evolution [223], Bayesian probability search [224], Estimation-of-Distribution...
Huazhong University of Science and Technology, Wuhan 430074, China a r t i c l e i n f o a b s t r a c t Keywords: In this paper, we proposed an effective genetic algorithm for solving the flexible job-shop scheduling Genetic algorithm problem (FJSP) to minimize makespan time. ...
Problems surrounding key indices, such as sensitivity enhancement, scanning range extension, and sidelobe level suppression, need to be solved urgently. Here, we propose a sparse optimization scheme based on a genetic algorithm for a 64-array element planar radio antenna array. As optimization ...
For solving the NW-FSSWST problems, the genetic algorithm is hybridized. A genetic algorithm (GA) is an effective population-based meta-heuristic method for solving combinatorial optimization problems. But, GA has got some disadvantages steps during the run time. For example, GA can sticks into ...
For both the single objective optimization problems, our algorithm reported better results than what was reported in the literature (Buitrago et al., 1996). We have also solved the multiobjective versions of the problem as such an approach gives an overview of how many BPD can be produced ...
As a technology, genetic algorithm is used in the process of automatic test cases. According to the existing research, evolutionary testing is often called genetic algorithm in the literature. Software testing is one of the main feasible methods to increase programmers’ great confidence in the ...
The aim of this study was to determine the optimal size of a downhole vortex tool in gas well with genetic algorithm for maximum gas-liquid separation effect and minimum pressure loss. The long term goal was to improve the effects of downhole vortex tools applied in water producing gas wells...
The basic steps of the genetic algorithm are shown in Figure 6. Open in figure viewerPowerPoint 5.3.1. Coding Design This model will use three layers of coding: (i) the selection of the least supplier in layer 1 (the 118 suppliers solved in the 5.2 model) will be coded from 0 to ...
Jia et al. [19] present a Modified Genetic Algorithm (MGA) to solve distributed scheduling problems in a multi-factory network, even if job delivery times are neglected. The algorithm is designed for fixed job routing and the flexibility issue is not taken into ac- ...
The present invention is a non-linear genetic algorithm for problem solving. The iterative process of the present invention operates on a population of problem solving entities. First, the activated entities perform, producing results. Then the results are assigned values and associated with the produ...