The real coded genetic algorithms (RCGA) have proved to be more efficient than traditional bit-string genetic algorithm in parameter optimization, but the RCGA focuses on crossover operators and loss on the mutation operator for local search. Evolution strategies (ESs) and evolutionary programming (EP) only concern the Gaussian mutation operators. This paper proposes a technique, called combined evolutionary algorithm (CEA), by incorporating the ideas of EP and GAs into ES. Simultaneously, we add the local competition into the CEA in order to reduce the complexity and maintain the diversity. Over 20 benchmark function optimization problems are taken as benchmark problems. The results indicate that the CEA approach is a very powerful optimization technique.
|Number of pages||6|
|State||Published - 1 Jan 1996|
|Event||Proceedings of the 1996 IEEE International Conference on Evolutionary Computation, ICEC'96 - Nagoya, Jpn|
Duration: 20 May 1996 → 22 May 1996
|Conference||Proceedings of the 1996 IEEE International Conference on Evolutionary Computation, ICEC'96|
|Period||20/05/96 → 22/05/96|