An integrated framework of hybrid evolutionary computations

There are various kinds of evolutionary computations (ECs) and they have their own merits and demerits. For example, PSO (particle swarm optimization) shows high ability during initial period in general, whereas DE (differential evolution) shows high ability especially in the latter period in search to find more accurate solutions. This paper proposes a novel and integrated framework to effectively combine the merits of several evolutionary computations. There are five distinctive features in the proposed framework. 1) There are several individual pools, and each pool corresponds to one EC. 2) Parents do not necessarily belong to the same EC: for example, a GA type individual can be a spouse of a PSO type individual. 3) Each incorporated EC has its own evaluated value (EV), and it changes according to the best fitness value at each generation. 4) The number of individuals in each EC changes according to the EV. 5) All of the individuals have their own lifetime to avoid premature convergence; when an individual meets lifetime, the individual reselect EC, and the probability of each EC to be selected depends on the EV. In the proposed framework, therefore, more individuals are allotted to the ECs which show higher performance than the other at each generation: effective usage of individuals is enabled. In this way, this framework can make use of merits of incorporated ECs. Original GA, original PSO and original DE are used to construct a simple proposed framework-based system. We carried out experiments using well-known benchmark functions. The results show that the new system outperformed there incorporated ECs in 9 functions out of 13 functions.