Genetic algorithm (GA), as an important intelligence computing tool, is a wide research content in the application domain and the academic circle now. This paper elaborates the improvement of premature convergence in GA used for optimizing multimodal numerical problems. Mutation is the principle operation in Genetic Algorithm (GA) for enhancing the degree of population diversity, but it is prov...

PURPOSE To genetically characterize a subphenotype of geographic atrophy (GA) in AMD associated with rapid progression and a diffuse-trickling appearance on fundus autofluorescence imaging. METHODS Patients from the Fundus Autofluorescence in Age-Related Macular Degeneration Study were phenotyped for diffuse-trickling GA (dt-GA; n = 44). DNA was analyzed for 10 known AMD-associated genetic va...

The job-shop scheduling problem (JSSP) is well known as one of the most difficult NP-hard combinatorial optimization problems. Genetic Algorithms (GAs) for solving the JSSP have been proposed, and they perform well compared with other approaches [1]. However, the tuning of genetic parameters has to be performed by trial and error, making optimization by GA ad hoc. To address this problem, Sawai...

Reader network planning (RNP) problem of radio frequency identification (RFID) system is a combinational optimization problem. In this study, we propose a genetic algorithm (GA) to solve this RNP problem. We have tested the proposed GA on several RNP problems and compare with a particle swarm optimization (PSO) method by solving the same RNPs. The comparison results demonstrate that the propose...

Genetic Algorithms are efficient search and optimization techniques inspired by natural evolution. To show the difficulties in solving constrained optimization problems through GA, the 0/1 knapsack problem with user specific object preferences has been taken up. A new genetic operator, namely, ‘gene silencing’ inspired from biology is used along with standard GA. The experimental results for va...

among various heuristics techniques, genetic algorithm (ga) is one of the most widely used techniques which has successfully been applied on a variety of complex combinatorial problems. the performance of ga largely depends on the proper selection of its parameters values; including crossover mechanism, probability of crossover, population size and mutation rate and selection percent. in this p...