Notice of RetractionA Predator-Prey Cellular Genetic Algorithm for Dynamic Optimization Problems

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

This paper proposes a predator-prey cellular genetic algorithm to solving dynamic optimization problems. A predator-prey model replaces the evolution rule in regular cellular genetic algorithm, which is more similar to the evolution scheme in real world. It contains two different populations: predator and prey, both of them are dynamic changes with predatory operation. The predators and preys are placed in two grid-world with same dimension; each predator capture the weakest prey in its neighborhood; the anticipant number of the living predator individuals is related to the number of prey individuals before the predator captured; the anticipant number of the living prey individuals is related to the number of predator individuals, and the number of null gird before the predator captured. Three dynamic optimization problems with different complexity are selected to verify the algorithm performance. The computation results indicate that the new algorithm has the approving performance in dealing with the dynamic optimization problems.