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.
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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.
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