MEC dissimilation strategy by rejected regions
Chengyi Sun, Junli Wang, Jianqing Zhang · 2002
Mind evolutionary computation (MEC) is a new approach to evolutionary computation (EC). This paper presents a new dissimilation strategy using rejected regions, which can avoid searching repeatedly, so that the capability of MEC to search globally in dissimilation is enhanced. Experimental results show that basic MEC has improved considerately compared with a genetic algorithm (GA), and that the MEC dissimilation strategy using rejected regions has also advanced a lot. The reason for this is that, in the modified MEC, the regions searched in similartaxis are recorded, so that, in dissimilation, the scope of scattered individuals is reduced to the whole solution space, excluding the rejected regions. Therefore, the regions explored in dissimilation have never been searched before, and the search scope is diminished accordingly, while the capability of MEC to search globally in dissimilation is enhanced and repeated searching is avoided. It is the memory mechanism of MEC that makes the dissimilation strategy of rejected regions possible, so the probability that the individuals are scattered in the region of the global optimum has greatly increased, the calculated amount and the average evaluation time are decreased, and population convergence can be implemented in fewer generations.