Over the years the benefits of searching an alternative space, namely, the heuristic space, instead of exploring the solution space directly has been shown. This has been achieved by using hyper-heuristics. As the field of hyper-heuristics has evolved since its inception there have been a number of advances in the field. This tutorial focuses on these advances specifically evolutionary algorithms. The tutorial firstly gives an overview of hyper-heuristics and then delves into the advanced topics. A taxonomy of the different levels of generality that can be attained by a hyper-heuristic is firstly presented. The tutorial then examines assessing hyper-heuristic performance in the context of these hyper-heuristics. The tutorial will then explore machine learning and evolutionary algorithm hyper-heuristics. While a lot of the earlier work in the field focused on discrete optimization, recent advancements include solving continuous optimization problems directly using hyper-heuristics. The tutorial will examine how hyper-heuristics can be used to directly solve continuous optimization problems. The tutorial will also highlight the benefits of transfer learning in evolutionary algorithm hyper-heuristics. Explainable hyper-heuristics, that is the use of XAI to better understand the performance of hyper-heuristics will be examined. These advances will be illustrated with EvoHyp a library for evolutionary algorithm hyper-heuristics which is available in Java and Python. The tutorial will conclude with a discussion session on future research in the field of evolutionary algorithm hyper-heuristics.

Tutorial breakdown:

1. Overview of Hyper-Heuristics

This tutorial firstly provides an overview of hyper-heuristics:

1.1 Selection construction hyper-heuristics
1.2 Selection perturbative hyper-heuristics
1.3 Generation construction hyper-heuristics
1.4 Generation perturbative hyper-heuristics

2. Taxonomy for generality levels in hyper-heuristic

Five levels of generality in hyper-heuristics will be described and a case study for each of the levels will be presented.

3. Assessing Hyper-Heuristic Performance

Assessing hyper-heuristic performance in terms of both generality and optimality will be discussed. An assessment measure for assessing generality, namely, standard deviation of differences, will be presented. Multi-objective assessment of hyper-heuristic performance will also be presented.

4. Machine Learning and Hyper-Heuristics

This part of the tutorial will examine the synergistic relationship between machine learning and evolutionary algorithm hyper-heuristics. Firstly, it will examine how machine learning can be used to improve the performance of hyper-heuristics and secondly how hyper-heuristics can be used to improve the performance of machine learning.

5. Evolutionary Algorithm Hyper-Heuristics for Continuous Optimization

Previous work using hyper-heuristics for solving continuous optimization problems have essentially used hyper-heuristics to design the approaches that are applied to the solve the problem. Here we will look at applying hyper-heuristics directly to solving the problem.

6. Transfer learning in Evolutionary Algorithm Hyper-Heuristics

This part of the tutorial will focus on using transfer learning in evolutionary algorithm hyper-heuristics. Case studies for the different types of hyper-heuristics will be examined.

7. Explainable Evolutionary Algorithm Hyper-Heuristics

The tutorial will examine the recent advances in the use of XAI to better understand and improve hyper-heuristic performance.

8. EvoHyp Demonstration

This part of the tutorial will involve a demonstration of EvoHyp an evolutionary algorithm library for hyper-heuristics.

9. Future research directions and discussion

This will involve an interactive session on future research directions in the field of evolutionary algorithm hyper-heuristics.