Name
Abstract | ||
|---|---|---|
Over the last two decades, many Genetic Algorithms have been introduced for solving optimization problems. Due to the variability of the characteristics in different optimization problems, none of these algorithms performs consistently over a range of problems. In this paper, we introduce a GA with a new multi-parent crossover for solving a variety of optimization problems. The proposed algorithm also uses both a randomized operator as mutation and maintains an archive of good solutions. The algorithm has been applied to solve the set of real world problems proposed for the IEEE-CEC2011 evolutionary algorithm competition. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/CEC.2011.5949731 | IEEE Congress on Evolutionary Computation |
Keywords | Field | DocType |
genetic algorithms,GA,IEEE-CEC2011 evolutionary algorithm competition problems,genetic algorithms,multiparent crossover,optimization problems,Numerical optimization,genetic algorithms | Particle swarm optimization,Mathematical optimization,Algorithm design,Crossover,Evolutionary algorithm,Computer science,L-reduction,Artificial intelligence,Operator (computer programming),Optimization problem,Genetic algorithm,Machine learning | Conference |
ISSN | ISBN | Citations |
Pending | 978-1-4244-7834-7 | 45 |
PageRank | References | Authors |
1.34 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Saber M. Elsayed | 1 | 74 | 2.54 |
| Ruhul A. Sarker | 2 | 115 | 5.21 |
| Essam, D.L. | 3 | 124 | 5.69 |