Multi-Objective Evolutionary Algorithms platform with support for flexible hybridization tools

TL;DR

The paper introduces Evogil, a flexible platform for multi-objective evolutionary algorithms that simplifies hybridization and customization of algorithms through a generalized meta-model and driver composition.

Contribution

It presents a novel platform that enables flexible hybridization of MOEAs by generalizing meta-models and supporting runtime combination of internal algorithms.

Findings

Supports custom hybrid models and runtime algorithm composition.

Provides ready-made solutions and processing tools.

Simplifies implementation of complex MOEA meta-models.

Abstract

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.