Knowledge-Intensive Evolutionary Algorithms for Solving a Healthcare Fleet Optimization Problem

Abstract

In this chapter, the authors show how knowledge engineering techniques can be used to guide the definition of evolutionary algorithms (EA) for problems involving a large amount of structured data, through the resolution of a real problem. Various representations of the fitness functions, the genome, and mutation/crossover operators adapted to different types of problems (routing, scheduling, etc.) have been proposed in the literature. However, real problems including specific constraints (legal restrictions, specific usages, etc.) are often overlooked by the proposed generic models. To ensure that these constraints are effectively considered, the authors propose a methodology based on the structuring of the conceptual model underlying the problem, as a labelled domain ontology suitable for optimization by EA. The authors show that a precise definition of the knowledge model with a labelled domain ontology can be used to describe the chromosome, the evaluation functions, and the crossover and mutation operators. The authors show the details for a real implementation and some experimental results.