Overview

Introduction

Hyper-heuristics is a rapidly developing domain which has proven to be effective at providing generalized solutions to problems and across problem domains. Evolutionary algorithms have played a pivotal role in the advancement of hyper-heuristics, especially generation hyper-heuristics. Evolutionary algorithm hyper-heuristics have been successful applied to solving problems in various domains including packing problems, educational timetabling, vehicle routing, permutation flowshop and financial forecasting amongst others. The aim of the tutorial is to firstly provide an introduction to evolutionary algorithm hyper-heuristics for researchers interested in working in this domain. An overview of hyper-heuristics will be provided including the assessment of hyper-heuristic performance. The tutorial will examine each of the four categories of hyper-heuristics, namely, selection constructive, selection perturbative, generation constructive and generation perturbative, showing how evolutionary algorithms can be used for each type of hyper-heuristic. A case study will be presented for each type of hyper-heuristic to provide researchers with a foundation to start their own research in this area. The EvoHyp library will be used to demonstrate the implementation of a genetic algorithm hyper-heuristic for the case studies for selection hyper-heuristics and a genetic programming hyper-heuristic for the generation hyper-heuristics. A theoretical understanding of evolutionary algorithm hyper-heuristics will be provided. A new measure to assess the performance of hyper-heuristic performance will also be presented. Challenges in the implementation of evolutionary algorithm hyper-heuristics will be highlighted. An emerging research direction is using hyper-heuristics for the automated design of computational intelligence techniques. The tutorial will look at the synergistic relationship between evolutionary algorithms and hyper-heuristics in this area. The use of hyper-heuristics for the automated design of evolutionary algorithms will be examined as well as the application of evolutionary algorithm hyper-heuristics for the design of computational intelligence techniques. The tutorial will end with a discussion session on future directions in evolutionary algorithms and hyper-heuristics. 

Aims of the Tutorial

The tutorial will aim to:  

  • Provide a sufficient introduction and overview of evolutionary algorithm hyper-heuristics to enable researchers to start their own research in this domain. 
  • Provide an overview of recent research directions in evolutionary algorithms and hyper-heuristics. 
  • Highlight the benefits of evolutionary algorithms to the field of hyper-heuristics.
  • Highlight the benefits and hyper-heuristics to evolutionary algorithms. 
  • Examine the assessment of hyper-heuristic performance.
  • Examine the theoretical aspects of evolutionary algorithm hyper-heuristics.
  • Stimulate interest and discussion on future research directions in the area of evolutionary algorithms and hyper-heuristics. 

Presenter Details

Nelishia Pillay

Department of Computer Science, University of Pretoria, South Afriica

E-mail: nelishia.pillay at up.ac.za