Multi-Objective Evolutionary Algorithms platform with support for flexible hybridization tools
This provides a tool for researchers and practitioners in evolutionary computation to more easily build and test custom hybrid MOEA models, though it is incremental as it builds on existing meta-model concepts.
The authors tackled the complexity of implementing and configuring multi-objective evolutionary algorithm (MOEA) meta-models by developing the Evogil platform, which simplifies simulation by allowing flexible hybridization of meta-models and drivers without redundant implementations.
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.