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More recently, he focused on the use of multi-objective approaches to tackle learning problems like premature convergence or generalization. Researchers of the team work on different aspects of learning in the context of motion control and cognition, both from a computational neuroscience perspective and a robotics perspective. His interests include computational intelligence in games, neuroevolution, evolutionary robotics and human computation. The intersection of culture, science and technology is attracting increasingly more public attention, with frequent exhibitions, competitions and industrial involvement worldwide.

The Digital Entertainment Technologies and Arts DETA track at GECCO, in its ninth edition in , focusses on the key application fields of arts, music, and games from the perspective of evolutionary computation, biologically inspired techniques, and more generally computational intelligence.

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We invite submissions describing original work involving the use of computational intelligence techniques in the creative arts, including design, games, and music. Works of a methodological, experimental, or theoretical nature within the context of digital entertainment will be considered. Vanessa Volz is a post-doctoral research associate at Queen Mary University London, UK, with focus in computational intelligence in games.

She received her PhD in from TU Dortmund University, Germany, for her work on surrogate-assisted evolutionary algorithms applied to game optimisation. She holds B. She received an M. Her current research focus is on employing surrogate-assisted evolutionary algorithms to obtain balance and robustness in systems with interacting human and artificial agents, especially in the context of games. The ECOM track aims to provide a forum for the presentation and discussion of high-quality research on metaheuristics for combinatorial optimization problems.

Challenging problems from a broad range of applications, including logistics, network design, bioinformatics, engineering and business have been tackled successfully with metaheuristic approaches. In many cases, the resulting algorithms represent the state-of-the-art for solving these problems. In addition to evolutionary algorithms, the class of metaheuristics includes prominent generic problem solving methods, such as tabu search, iterated local search, variable neighborhood search, memetic algorithms, simulated annealing, GRASP and ant colony optimization.

The ECOM track encourages original submissions on all aspects of evolutionary combinatorial optimization and metaheuristics, including, but not limited to:. Besides topics in swarm intelligence, his research interests are mainly focused on the hybridization of metaheuristics with other techniques for optimization. He has co- authored more than publications in international journals, books, and peer-reviewed conference proceedings.

Moreover, he is a co-founder of the workshop series on Hybrid Metaheuristics. His research interests and publications include the landscapes theory of combinatorial optimization problems and the application of theoretical results to the design of new search algorithms and operators. Evolutionary methods can tackle many different tasks within the ML context, including problems related to supervised, unsupervised, semi-supervised, and reinforcement learning, as well as emergent topics such as transfer learning and domain adaptation, deep learning, learning with a small number of examples, and learning with unbalanced data and missing data.

ACO-SI - Ant Colony Optimization and Swarm Intelligence

The tasks range from classification, via clustering, regression, prediction to time series analysis and ML problems. The global search performed by evolutionary methods frequently provides a valuable complement to the local search of non-evolutionary methods and combinations of the two often show particular promise in practice. This track aims to encourage information exchange and discussion between researchers with an interest in this growing research area. We encourage submissions related to theoretical advances, the development of new or modification of existing algorithms, as well as application-focused papers.

She is with the Evolutionary Computation Research Group at VUW, and her research focuses mainly on evolutionary computation, machine learning and data mining, particularly, evolutionary computation for feature selection, feature construction, dimension reduction, symbolic regression, multi-objective optimisation, bioinformatics and big data. Bing is currently leading the strategic research direction on evolutionary feature selection and construction in Evolutionary Computation Research Group at VUW, and has been organsing special sessions and issues on evolutionary computation for feature selection and construction.

Jean-Baptiste Mouret is a senior researcher "Directeur de recherche" at Inria, the French research institute dedicated to computer science and mathematics. Overall, J. Mouret conducts researches that intertwine evolutionary algorithms, neuro-evolution, and machine learning to make robots more adaptive. His work was recently featured on the cover of Nature Cully et al. In many real-world applications, several objective functions have to be optimized simultaneously, leading to a multiobjective optimization problem MOP for which an ideal solution seldom exists.

Rather, MOPs typically admit multiple compromise solutions representing different trade-offs among the objectives. Due to their applicability to a wide range of MOPs, including black-box problems, evolutionary algorithms for multiobjective optimization have given rise to an important and very active research area, known as Evolutionary Multiobjective Optimization EMO. No continuity or differentiability assumptions are required by EMO algorithms, and problem characteristics such as nonlinearity, multimodality and stochasticity can be handled as well.

Furthermore, preference information provided by a decision maker can be used to deliver a finite-size approximation to the optimal solution set the so-called Pareto-optimal set in a single optimization run. The Evolutionary Multiobjective Optimization EMO Track is intended to bring together researchers working in this and related areas to discuss all aspects of EMO development and deployment, including but not limited to :. In , he was a post-doctoral researcher at the University of Coimbra Portugal.

His main research activities deal with the foundations, the design and the analysis of stochastic local search heuristic algorithms, with a particular interest in multiobjective optimization. He co-authored more than fifty scientific papers in international journals, book chapters and international conferences. He has held postdoctoral positions as a Research Fellow working on the interface of Bayesian modelling and optimisation and as a Business Fellow focusing on knowledge transfer to industry prior to his appointment to an academic position at Exeter.

He has published widely on theoretical and applied aspects of evolutionary multi-objective optimisation, and also in the field of machine learning — and has ongoing interests on the interface between these areas. His theoretical work includes algorithm development and analysis, along with data structures required for efficient multi-objective optimisation and Pareto set maintenance.

His applied work includes costly and uncertain industrial design problems, air traffic control safety systems, automating biological experiments and robust multi-objective routing.

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He has been active within the evolutionary computation community as a reviewer and program committee member since Work that advances experimental methodology and benchmarking, problem and search space analysis is also encouraged. His research areas are evolutionary computation, machine learning and probabilistic graphical models and its application in the solution of real problems in biomedicine, industry or finance.

He has published 4 books, more tan scientific ISI journal articles and about contributions to national and international conferences. These publications have received more than citations. His research interests span evolutionary computation, numerical optimization, and machine learning. We invite submissions to the GA track that present original work on all aspects of genetic algorithms, including, but not limited to:.

She worked in industry for five years before joining academia and has held faculty and research positions at the University Simon Bolivar, Venezuela and the University of Nottingham, UK. Her research interests lie in the foundations and application of evolutionary algorithms and heuristic search methods, with emphasis on autonomous search, hyper-heuristics, fitness landscape analysis and visualisation.

Tian-Li Yu received the B. Currently, he is working as an associate professor in the National Taiwan University. He has been doing research in the field of Evolutional Computation for about 15 years.

Robustness (computer science)

General Evolutionary Computation and Hybrids is a new track that recognises that Evolutionary Algorithms are often used as part of a larger system, or together in synergy with other algorithms. We welcome high quality papers on a range of topics that might not fit solely into any of the other track descriptions. Holger H. Holger's research interests span artificial intelligence, empirical algorithmics, bioinformatics and computer music.

He is known for his work on machine learning and optimisation methods for the automated design of high-performance algorithms and for his work on stochastic local search. Based on a broad view of machine learning, he has developed - and vigorously pursues - the paradigm of programming by optimisation PbO ; he is also one of the originators of the concept of automated machine learning AutoML.

Holger has a penchant for work at the boundaries between computing science and other disciplines, and much of his work is inspired by real-world applications.

Ebook Self Evolvable Systems Machine Learning In Social Media

The initiative has attracted major media coverage in many European countries and garnered broad support by more than AI experts, more than one hundred fellows of various scientific AI associations, many editors of scientific AI journals, national AI societies, top AI institutes and key stakeholders in industry and other organisations for details, see claire-ai. Yaochu Jin received the B. Degree from Ruhr University Bochum, Germany, in His research interests include data-driven evolutionary optimization, Bayesian optimization, secure and interpretable machine learning, evolutionary multi-objective learning, evolutionary developmental systems, and neural plasticity.

Various representations have been used in GP, such as tree-structures, linear sequences of code, graphs and grammars. Provided that a suitable fitness function is devised, computer programs solving the given problem emerge, without the need for the human to explicitly program the computer. The GP track invites original submissions on all aspects of the evolutionary generation of computer programs or other executable structures for specified tasks. John's, Canada. She is interested in understanding the fundamental mechanisms of evolution, especially the properties of robustness and evolvability of evolutionary algorithms and novel applications of evolutionary algorithms in biomedical research.

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The aim is to bring together contributions from the diverse application domains into a single event. The focus is on applications including but not limited to:. All contributions should be original research papers demonstrating the relevance and applicability of EC within a real-world problem. Papers covering multiple disciplines are welcome; we encourage the authors of such papers to write and present them in a way that allows researchers from other fields to grasp the main results, techniques, and their potential applications.

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He received the MEng degree in Control Systems Engineering in and a PhD in Control Systems in for his research on evolutionary multi-objective optimization both from the University of Sheffield. His research interests are in methods for design optimization, with a focus on decision support for real-world applications. He has successfully applied evolutionary algorithms to many-objective, robust and distributed optimization problems in projects with leading manufacturers of complex engineered products, such as Jaguar Land Rover and Ford Motor Company. Robin also has research interests in the modelling and simulation of complex social systems, with a focus on health behaviours.

In addition to his interest in the fundamental working principles of evolutionary algorithms, Thomas has ample experience in real-life applications of optimization and predictive analytics, through working with global companies such as BMW, Daimler, Honda Research Institute, KLM, Tata Steel. The Washington Institute is to demand a powerful and several impact of first officials in the Middle East and to have the findings that are them.

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