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Tutorial

 
Evolutionary Algorithms and Hyper-Heuristics
Lecturer: Nelishia Pillay, University of KwaZulu-Natal, South Africa
 
 
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.
 
 
Nature-Inspired Optimization Algorithms
Lecturer: Xin-She Yang, Middlesex University, United Kingdom
 
 
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.
 
 
Keynote Lectures

 
Perpetual Motion, Evolutionary Computation in Industry and other Chimeras
Anna Esparcia-Alcázar, Universitat Politècnica de València, Spain
 
Can you apply Computational Intelligence in industry? Is there Evolutionary Computation life outside Academia? Will Benson care? I'll try to find answers to these and other questions with a few reflections from my own history.
 
AI Researchers, Video Games are your Friends!
Julian Togelius, New York University, United States
 
Artificial intelligence and games go way back. At least to Turing, who re-invented the Minimax algorithm to play Chess even before he had a computer, and to Samuel, who invented a predecessor of TD-learning in order to build a Checkers-playing program in the 1950s. Games are important for AI because they are designed to challenge and train human cognitive capabilities, and are thus uniquely relevant benchmark problems. They are also uniquely convenient benchmark problems, as they allow unbiased comparison between algorithms and can be executed thousands of times fast than realtime. But one should also not forget the financial clout of the games industry and games' appeal for new students. While research on board games such as Chess and Go has been part of AI research since its inception, the last decade has seen the rise of a research community around AI for video games, and not only for playing them. In this talk I will outline some of the most important trends in recent years. One is General Video Game Playing: developing controllers that can learn to play not just a single game, but a large variety variety of them. Another is Procedural Content Generation, where AI algorithms are used to generate content for games, or even design the games themselves. Yet another trend is AI-assisted design tools, which provide the game designer with instant feedback and suggestions and thus scaffolds the game design process. These research topics inform each other, with general video game playing algorithms being important for procedural content generation and AI-assisted design tools. Finally, I will try to convince you that your own research is important for this endeavor and that you should consider steering your research towards AI for games.
 
Posters Session

 
 
Today 13 posters were scheduled to be presented in a forum for presenters from around the world to highlight their work and to share their successful ideas with colleagues by presenting a research study, a practical problem-solving effort, or an innovative idea. The Poster presentations provided other conference participants an opportunity to quickly and easily become acquainted with the presenters’ poster topic.
 
Closing Session

 
 
At the closing session, A "Best Paper Award" and a "Best Student Paper Award" were conferred to the author(s) of a full paper presented at the conference, selected by the Program/Conference Chairs based on the best combined marks of paper reviewing, assessed by the Program Committee, and paper presentation quality, assessed by session chairs at the conference venue.
 
Farewell Cocktail

 
 
Time to say goodbye and what a great way of doing it. Enjoy a laugh with new and old friends and we hope to have your presence at the conference’s next edition.
 
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