For this problem, we propose a genetic algorithm using an optimized crossover operator to find an optimal schedule which minimizes the maximum lateness of the jobs in the presence of the sequence independent fa
An optimized crossover and optimized mutation chooses the best combination of multiple relations of joins contains spatial data. Experimental evaluations are carried out with both synthetic and real datasets to estimate the performance of the proposed optimized genetic algorithm for multi-join relation ...
Crossover: The selected parents are combined through a crossover operator to create offspring solutions that inherit some characteristics from each parent. 5) Mutation: Some offspring solutions undergo a mutation operator that randomly changes some VMD parameters to introduce new variations in the popula...
Kim B W. Modeling of thin film process data using a genetic algorithmoptimized initial weight of backpropagation neural network[J]. Applied Artificial Intelligence, 2009, 23(2): 168-178....
This research paper proposes a 16-order IIR bi-quad filter optimized with evolutionary algorithm in the form of genetic crossover and mutation to provide maximum gain. The magnitude, frequency response and group delay characteristics obtained from the proposed algorithm have been tested and analyzed ...
used Genetic Algorithm (GA) in Software Product Line (SPL) for the feature optimization. They used population size of 100, roulette wheel, one-point crossover with the probability of 0.8 and 1% for the mutation as features of GA. At the result, it was discovered that GA produce fast and...
For instance, genetic algorithms optimize network structures by simulating biological evolution processes like crossover and mutation [17]. Particle swarm optimization, on the other hand, finds optimal solutions by mimicking the social behavior of bird flocks or fish schools [18]. This research ...
The optimization process is implemented via the Genetic Algorithm Toolbox of MATLAB. Here, the initial population is randomly generated individuals within a constrained domain which is considered from the practical consideration. Three main operations, selection crossover and mutation, are used at each ...
Poon PW, Carter JN (1995) Genetic algorithm crossover operations for ordering applications. Comput Oper Res 22:135–147 CrossRef Wen X, Song A (2003) An improved genetic algorithm for planar and spatial straightness error evaluation. Int J Mach Tools Manuf 43:1157–1162 CrossRef Ye Z, ...
Genetic algorithm uses the group search technology and takes population on behalf of the solution of a group questions. By doing a series of genetic operations like selection, crossover, mutation, and so on to produce the new generation population, and gradually evolve until getting the optimal ...