Tutorials: Evolutionary Algorithms and Hyper-Heuristics

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.

The tutorial will be divided into three parts. The first will cover introductory topics and provide researchers with a foundation to start research in this domain. The second section covers the emerging research direction of hyper-heuristics and automated design. Hyper-heuristics for evolutionary algorithm design and evolutionary algorithm hyper-heuristics for automated design. The last part is a discussion session looking at future research directions in evolutionary algorithms and hyper-heuristics.
Part I
1. An Overview of Hyper-Heuristics
The section firstly presents low-level heuristics leading to a description of hyper-heuristics. This is followed by a classification of hyper-heuristics which will introduce the four types of hyper-heuristics, namely, selection constructive, selection perturbative, generation constructive and generation perturbative. The section will conclude by examining measures for the performance of hyper-heuristic performance.
1.1 Low-Level Heuristics
1.2 Classification of Hyper-Heuristics
1.3 Assessment of Hyper-Heuristic Performance
2. Evolutionary Algorithm Hyper-Heuristics
This section will describe details of the evolutionary algorithms used and applications for each type of hyper-heuristic. A case study will be presented for each type of hyper-heuristic to illustrate how the hyper-heuristic can be applied. The EvoHyp Java evolutionary algorithm hyper-heuristic library will be used to demonstrate the implementation of evolutionary algorithm hyper-heuristics for each case study. A theoretical understanding of evolutionary algorithm hyper-heuristics will be presented. The section concludes by looking at the challenges associated with the implementation of evolutionary algorithm hyper-heuristics and potential solutions.2.1 Selection Constructive Hyper-Heuristics 2.2 Selection Perturbative Hyper-Heuristics 2.3 Generation Constructive Hyper-Heuristics 2.4 Generation Perturbative Hyper-Heuristics 2.5 Theoretical Analysis
2.6 Challenges
Part II
3. Hyper-Heuristics for Evolutionary Algorithm Design
Hyper-heuristics have proven to be effective in the design of evolutionary algorithms. This has ranged from parameter tuning, selection of operators, to generation of operators and algorithm components. This section will provide a synopsis of how evolutionary algorithms can be designed using hyper- heuristics.4. Evolutionary Algorithm Hyper-Heuristics for Design
One of the recent research directions in the area of hyper-heuristics is the use of hyper-heuristics for design. This section provides an account of the use of evolutionary algorithm hyper-heuristics for design. An overview of how evolutionary algorithms can be used for the design of algorithms and techniques such as metaheuristics, and example applications will be provided.
Part III
5. Discussion Session: Future Research Directions