F. Herrera and M. Lozano, "Gradual distributed real-coded genetic algorithms," IEEE Trans. on Evolutionary Computation, vol. 4, no. 1, pp. 43--62, April 2000. Home/Search Document Details and Download
Summary Related Articles Check |
This paper is cited in the following contexts:
Parallel Heterogeneous Genetic Algorithms for Continuous.. - Alba, Luna, Nebro (Correct)
.... a set of tentative solutions (population of individuals) by applying crossover operators (merging two or more parents to yield one or more offsprings) and mutations of their contents (random alterations of the problem variables) The gradual distributed real coded GA (GD RCGA) model of search [8] is a kind of distributed technique that runs eight populations concurrently in a cubic topology with sparse migrations of individuals among them. Distributed algorithms are a subclass of decentralized evolutionary algorithms [1] aimed at reducing the convergence to local optima, promoting ....
....time point of view in Section 4. Finally, we summarize the conclusions and discuss several lines for future research in Section 5. 2 Gradual Distributed Real Coded Genetic Algorithms In this section we describe the basic behavior of the gradual distributed real coded genetic algorithm (GDRCGA) [8], and explain how we have parallelized it as a set of concurrent objects using the JACO environment [12] in Java. 2.1 GD RCGA The present availability of crossover operators for realcoded genetic algorithms (RCGAs) allows the possibility of including in the same algorithm different exploration ....
[Article contains additional citation context not shown here]
F. Herrera and M. Lozano. Gradual distributed real-coded genetic algorithms. IEEE Transactions on Evolutionary Computation, 4(1):43--63, 2000.
Parallel Genetic Algorithm with Parameter Adaptation - Tongchim, Chongstitvatana (2002) (2 citations) (Correct)
....random algorithm: At the beginning, each subpopulation randomly creates its own parameter set. This algorithm is comparable to the adaptive algorithm without the parameter adaptation. The similar techniques of using di#erent parameter sets on the multiple subpopulations were also presented in [15,16]. However, their parameters in each subpopulation were not random. 4. Static algorithm: This algorithm uses a static parameter set from the study by De Jong [7] The following four parameters are involved in the experiments. 1. Crossover operator : The five crossover types used in this study are ....
F. Herrera and M. Lozano. Gradual distributed real-coded genetic algorithms. IEEE Transactions on Evolutionary Computation, 4(1):43--63, April 2000.
Jinetic: a distributed, fine-grained, asynchronous.. - Atienza, Garca.. (2000) (Correct)
....by [4] or [5] to a coarsegrained model, in which a computer acts as host and the rest of the computers 1 take orders from it (as proposed by [6] However, what is basically parallelized here is the evaluation of the chromosomes, not the rest of the operations. A similar approach is used in [7], where populations are heterogeneous, using di erent kind of operators, which take advantage of exploration or exploitation. Asynchronous genetic algorithms were also proposed in systems like ASPARAGOS (Asynchronous parallel genetic optimization system) 5] in this work each individual is ....
F. Herrera and M. Lozano. Gradual distributed real-coded genetic algorithms. Technical report, Dept. of Computer Science and Articial Intelligence, University of Granada, 1997. Technical Report 97-01-03. 4
The Influence of Grid Shape and Asynchronicity On.. - Dorronsoro, Alba, .. (2004) (Correct)
No context found.
F. Herrera and M. Lozano, "Gradual distributed real-coded genetic algorithms," IEEE Trans. on Evolutionary Computation, vol. 4, no. 1, pp. 43--62, April 2000.
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC