Journal of Global Optimization
A Trigonometric Mutation Operation to Differential Evolution
Journal of Global Optimization
A Note on the Extended Rosenbrock Function
Evolutionary Computation
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
A new generation alternation model for differential evolution
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Statistical Analysis with Excel For Dummies
Statistical Analysis with Excel For Dummies
Differential Evolution: In Search of Solutions (Springer Optimization and Its Applications)
Differential Evolution: In Search of Solutions (Springer Optimization and Its Applications)
Advances in Differential Evolution
Advances in Differential Evolution
Statistical analysis of the main parameters involved in the designof a genetic algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
System design by constraint adaptation and differential evolution
IEEE Transactions on Evolutionary Computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Statistical exploratory analysis of genetic algorithms
IEEE Transactions on Evolutionary Computation
Accelerating Differential Evolution Using an Adaptive Local Search
IEEE Transactions on Evolutionary Computation
Comparative study of extended sequential differential evolutions
ACE'10 Proceedings of the 9th WSEAS international conference on Applications of computer engineering
Concurrent implementation of differential evolution
ISTASC'10 Proceedings of the 10th WSEAS international conference on Systems theory and scientific computation
A comparative study of structured differential evolutions
ACS'10 Proceedings of the 10th WSEAS international conference on Applied computer science
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Differentiation Evolution (DE) is an Evolutionary Algorithm (EA) for solving function optimization problems. In order to renew the population in EA, there are two generation models. The first one is "discrete generation model", and the second one is "continuous generation model". Conventional DEs have been based on the discrete generation model in which the current generation's population is replaced by the next generation's population at a time. In this paper, a novel DE based on the continuous generation model is described. Because a newborn excellent individual is added to an only population and can be used immediately to generate offspring in the continuous generation model, it can be expected that the novel DE converges faster than the conventional ones. Furthermore, by employing the continuous generation model, it becomes easy to introduce various survival selection methods into DE. Therefore, three survival selection methods are contrived for the novel DE based on the continuous generation model. Finally, the effects of the generation model, the survival selection method, the reproduction selection method, the population size and their interactions on the performance of DE are evaluated statistically by using the analysis of variance (ANOVA).