Recurring multistage evolutionary system for balancing exploration and exploration

Abstract

Maintaining proper balance between exploitative and explorative operations of an evolutionary algorithm is essential for preventing premature convergence to local optima and for sustaining sufficient convergence speed throughout the evolution. This thesis introduces Recurring Multistage Evolutionary Algorithm (RMEA), a completely new framework to balance the exploitative and explorative features of the conventional evolutionary algorithm. The basis of RMEA is repeatedly alternating three different stages of evolution, each with its oWn explorative or exploitative objective and genetic operators. As the stages of RMEA repeat, the conflicting goals of exploitation and exploration are distributed gracefully across the generations of the different stages. The key concept of RMEA is to combine dissimilar information across the population for search space exploration and to combine similar information within population neighborhood for local exploitation. Performance of RMEA has been evaluated on a number of benchmark numerical optimization problems and results are compared with several existing algorithms. Experimental results show that RMEA performs better optimization with a higher rate of convergence for most of the problems. Also, an in-depth experimental study is carried out about the roles of the different stages and operators of RMEA, as well as the sensitivity of its parameters. An adaptive variant of RMEA is also proposed, which adjusts its parameters in an adaptive manner during the evolution, and does not require any problem specific knowledge from the user.