In present paper, a novel evolutionary metaheuristic designed for solving multimodal problems is presented. It is metaphorically named Genetic Chromodynamics (GC) as all individuals in the initial population are supposed to have different colors and, during evolution, dynamics in the colors of the population is achieved through the use of the classical evolutionary operators and a newly introduces merging operator. The latter has the property of reducing population size by the removal of the qualitatively poor individuals. In addition, Genetic Chromodynamics uses a local interaction principle as selection for reproduction. Proposed evolutionary paradigm has a high success rate and a good running time, as the number of fitness evaluations is reduced together with population size. Results obtained for function optimization, clustering, classification and multiobjective optimization underline the fact that GC is a powerful metaheuristic able to solve various kind of problems.