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-ofthe-
art parameter tuning (SPO), encourage further investigation.