A new radii-based evolutionary algorithm (EA) designed for multimodal optimization problems is proposed. The approach can be placed within the genetic chromodynamics framework and related to other EAs with local interaction, e.g. using species formation or clearing procedures. The underlying motivation for modifying the original algorithm was to preserve its ability to search for many optima in parallel while increasing convergence speed, especially for complex problems, through generational selection and different replacement schemes. The algorithm is applied to function optimization and classification; obtained experimental results, in part improved immensely by state-of-the-art parameter tuning (SPO), encourage further investigation.