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 proposed based on the predatorprey
relationship in real world. In grid-world, each predator captures the weakest prey in its neighborhood. The population
size of predator and prey scheme is researched. Orthogonal crossover operator is introduced to further improve the search
ability of the algorithm. Three dynamic optimization problems with different complexity are selected to verify the algorithm
performance. The computation results show that the proposed algorithm has the better performance in dealing with the
dynamic optimization problems.