A dynamic history-driven evolutionary algorithm

Dynamic objective problem (DOP) raises two challenging issues to evolutionary algorithm: comparing two individuals evaluated at different time instances and tracing the jumping global optimum. This paper presents a dynamic objective evolutionary algorithm (DOEA) that handles these issues through search history. The presented algorithm, namely dynamic objective history driven evolutionary algorithm (DyHdEA), stores the entire search history including the position, the fitness and the evaluated time of the solutions in a dynamic fitness tree. In the experiment section, DyHdEA is examined on a 10-dimensional DOP that is composed of five basis problems ranging from uni-modal to multi-modal, and from separable to non-separable. Meanwhile, the performance of DyHdEA is compared with five benchmark DOEAs including artificial immune algorithm, differential evolution, evolutionary programming, and particle swarm optimization. Seen from the result, DyHdEA effectively traces the dynamic global optimum with jumping transitions.