  Working with complex, high-level MOEA meta-models such as Multiobjec-tive
Optimization Hierarchic Genetic Strategy (MO-mHGS) with multi-deme support
usually requires dedicated implementation and configuration for each internal
(single-deme) algorithm variant. If we generalize meta-model, we can simplify
whole simulation process and bind any internal algorithm (we denote it as a
driver), without providing redundant meta-model implementations. This idea has
become a fundamental of Evogil platform. Our aim was to allow construct-ing
custom hybrid models or combine existing solutions in runtime simulation
environment. We define hybrid solution as a composition of a meta-model and a
driver (or multiple drivers). Meta-model uses drivers to perform evolutionary
calculations and process their results. Moreover, Evogil provides set of
ready-made solutions divided into two groups (multi-deme meta-models and
single-deme drivers), as well as processing tools (quality metrics, statistics
and plotting scripts), simulation management and results persistence layer.
