Evolutionary Algorithms and Hyper-Heuristics
Instructor
|
Nelishia Pillay
University of KwaZulu-Natal
South Africa
|
|
|
|
Brief Bio
Nelishia Pillay is an Associate Professor in the School of Mathematics, Statistics and Computer Science at the University of KwaZulu-Natal. She holds a Phd in Computer Science from the University of KwaZulu-Natal. Her research areas include hyper-heuristics, combinatorial optimization, genetic programming, genetic algorithms and other biologically-inspired methods. She has published in these areas in journals, national and international conference proceedings. She is a member of the IEEE Task Force on Hyper-Heuristics with the Technical Committee of Intelligent Systems and Applications at IEEE Computational Intelligence Society and the Technical Committee on Soft Computing under the IEEE Systems, Man, and Cybernetics Society. She has served on program committees for numerous national and international conferences and is a reviewer for various international journals. She is an active researcher in field of evolutionary algorithm hyper-heuristics for combinatorial optimization and design. This is one of the focus areas of the NICOG (Nature-Inspired Computing Optimization) research group which she has established.
|
Abstract
ABSTRACT
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 hyperheuristics. 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. 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-heuristics to provide researchers with a foundation to start their own research in this area. Challenges in the implementation of evolutionary algorithm hyper-heuristics will be highlighted. The tutorial will also look at recent and emerging research directions in evolutionary algorithm hyper-heuristics. Two areas in particular will be focused on, namely, evolutionary algorithm hyper-heuristics for algorithm design and the use of hyperheuristics for designing evolutionary algorithms. The tutorial will end with a discussion session on future directions in evolutionary algorithms and hyper-heuristics.
FORMAT
This is a tutorial divided into three parts. The first will cover introductory topics and provides researchers with a foundation to start research in this domain; the second section covers recent and emerging directions in the field, focusing specifically on evolutionary algorithm hyperheuristics for design and hyper-heuristics for evolutionary algorithm design; the last part is a discussion session looking at future research directions in evolutionary algorithms and hyperheuristics:
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 hyperheuristics, namely, selection constructive, selection perturbative, generation constructive and generation perturbative. An overview of the two cross-domain challenges (CHeSC 2012 and CHeSC 2014) organized by the hyper-heuristics community to promote generalization of hyperheuristics across problems domains will be provided. The section will conclude by looking at existing frameworks that researchers can use to develop their hyper-heuristics.
1.1 Low-Level Heuristics
1.2 Classification of Hyper-Heuristics
1.3 Cross-Domain Challenges
1.4 Hyper-Heuristic Frameworks
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 3 illustrate how the hyper-heuristic can be applied. 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 Challenges
Part II
3. Evolutionary Algorithm Hyper-Heuristics for Design.
One of the recent research directions in the area of hyper-heuristics is the use of hyperheuristics 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.
4. 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.
Part III
5. Discussion Session: Future Research Directions
Keywords
hyper-heuristics, evolutionary algorithms, algorithm design
Aims and Learning Objectives
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 hyperheuristics.
•Highlight the benefits of evolutionary algorithms to the field of hyper-heuristics and hyperheuristics to evolutionary algorithms.
•To stimulate interest and discussion on future research directions in the area of evolutionary algorithms and hyper-heuristics.
Target Audience
The tutorial is aimed at researchers in evolutionary algorithms who have an interest in hyperheuristics or have just started working in this area.
Prerequisite Knowledge of Audience
A background in evolutionary algorithms is assumed. A knowledge of both genetic algorithms and genetic programming would be ideal.
Detailed Outline
The tutorial will be divided into three parts. The first will cover introductory topics and provides researchers with a foundation to start research in this domain. The second section covers recent and emerging directions in the field, focusing specifically on evolutionary algorithm hyperheuristics for design and hyper-heuristics for evolutionary algorithm design. The last part is a discussion session looking at future research directions in evolutionary algorithms and hyperheuristics.
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 hyperheuristics, namely, selection constructive, selection perturbative, generation constructive and generation perturbative. An overview of the two cross-domain challenges (CHeSC 2012 and CHeSC 2014) organized by the hyper-heuristics community to promote generalization of hyperheuristics across problems domains will be provided. The section will conclude by looking at existing frameworks that researchers can use to develop their hyper-heuristics.
1.1 Low-Level Heuristics
1.2 Classification of Hyper-Heuristics
1.3 Cross-Domain Challenges
1.4 Hyper-Heuristic Frameworks
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 3 illustrate how the hyper-heuristic can be applied. 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 Challenges
Part II
3. Evolutionary Algorithm Hyper-Heuristics for Design
One of the recent research directions in the area of hyper-heuristics is the use of hyperheuristics 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.
4. 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.
Part III
5. Discussion Session: Future Research Directions
Nature-Inspired Optimization Algorithms
Instructor
|
Xin-She Yang
Middlesex University London
United Kingdom
|
|
|
|
Brief Bio
Xin-She Yang obtained his DPhil in Applied Mathematics from the University of Oxford. He then worked at Cambridge University and then later at National Physical Laboratory (UK) as a Senior Research Scientist. Now he is Reader in Modelling and Optimization at Middlesex University London, and a co-Editor of the Springer Tracts in Nature-Inspired Computing. He is also an elected Fellow of the Institute of Mathematics and its Applications (FIMA), UK. He was the IEEE Computational Intelligence Society (CIS) chair for the Task Force on Business Intelligence and Knowledge Management (2015 to 2020). He has published more than 40 books and more than 350 peer-reviewed research papers with more than 90,000 citations, and he has been on the prestigious list of most influential researchers or highly-cited researchers (Web of Sciences) for eight consecutive years since 2016.
|
Abstract
ABSTRACT:
Many problems in optimization and computational intelligence are very challenging to solve, and there is often no efficient algorithm to tackle hard problems. For such NP-hard problems, nature-inspired metaheuristic algorithms can be a good alternative approach, and such algorithms include particle swarm optimization (PSO), ant colony optimization (ACO), bat algorithm and firefly algorithms and others. Over the last two decades, nature-inspired optimization algorithms have become increasingly popular in solving large-scale, nonlinear, global optimization with many real-world applications. They also become an important of part of optimization and computational intelligence. These new so-called “smart algorithms” emerge almost every year, and this tutorial course will review and introduce some of the last developments.
FORMAT:
This course intends to introduce the fundamentals and latest advances of the state-of-the-art nature-inspired algorithms with the focus on the implementation and algorithm analysis.
We will introduce and discuss in detail most of the new metaheuristics.
- Nature-Inspired Algorithms (2 hours) - Introduce in detail state-of-the-art nature-inspired optimization, including
• ant and bee algorithm
• bat algorithm;
• cuckoo search;
• particle swarm optimization;
• firefly algorithm;
• harmony search and others.
- Implementations and Applications (1 hour) - Provide details of implementations and discussions of 10 well-chosen case studies, including
• Engineering Design Optimization;
• Travelling Salesman Problem;
• Clustering and Classifications;
• Image Segmentation, Feature Selection and others.
Keywords
Algorithm, bio-inspired computation, evolutionary computation, nature-inspired algorithms, metaheuristic, optimization.
Aims and Learning Objectives
To introduce the start-of-the-art nature-inspired optimization algorithms and their applications. To help researchers to use and implement new algorithms in their own research.
Target Audience
MSc and PhD students, researchers new in the field of computational intelligence, data mining, evolutionary computation and optimization.
Prerequisite Knowledge of Audience
Knowledge of basic calculus and matrix algebra.
Detailed Outline
This 3-hour course intends to introduce the fundamentals and latest advances of the state-of-the-art nature-inspired algorithms with the focus on the implementation and algorithm analysis. We will introduce and discuss in detail most of the new metaheuristics.
1. Nature-Inspired Algorithms (2 hours)
Introduce in detail state-of-the-art nature-inspired optimization, including
• ant and bee algorithm
• bat algorithm
• cuckoo search
• particle swarm optimization,
• firefly algorithm,
• harmony search and others
Implementations and Applications (1 hour)
Provide details of implementations and discussions of 10 well-chosen case studies, including
• Engineering Design Optimization
• Travelling Salesman Problem
• Clustering and Classifications
• Image Segmentation, Feature Selection and Others.