Thus, the detection and recognition system often needs thoroughoverhaul when applied to other types of images different from the one forwhich the system was designed.
This is very uneconomical and requires highlytrained experts. The purpose of incorporating learning into the system designis to avoid the time consuming process of feature generation and selection andto lower the cost of building object detection and recognition systems. Evolutionary computation is becoming increasingly important for computervision and pattern recognition fields. It provides a systematic way of synthesisand analysis of object detection and recognition systems.
With learningincorporated, the resulting recognition systems will be able to automaticallygenerate new features on the fly and cleverly select a good subset of featuresaccording to the type of objects and images to which they are applied. One can argue that evolutionary algorithm theory has concentrated on an aspect of performance that is at odds with real applications of evolutionary algorithms. A rather novel perspective, fixed budget computations, are a better match with the way evolutionary algorithms are applied.
The talk provides an overview of the state of the art in evolutionary algorithm theory , introduces fixed budget computations and explains how results from run time theory can systematically be transformed into results of this new and more useful type. He has published 19 journal papers, 40 conference papers, contributed seven book chapters and authored one book on evolutionary algorithm theory. His research is centred around design and theoretical analysis of artificial immune systems, evolutionary algorithms and other randomised search heuristics.
In he will be co-organising FOGA Differential evolution has recently become a or the most competitive real-parameter optimizer in diverse scenarios.
This talk will present some important parameter and operator adaptation methods used with differential evolution currently. The talk will also touch on a few different optimization problem scenarios such as single objective, multiobjective, dynamic, multimodal, etc. The talk will also identify some future research directions. Ponnuthurai Nagaratnam Suganthan received the B. A degree and M. He obtained his Ph.
His research interests include evolutionary computation, pattern recognition, multi-objective evolutionary algorithms, applications of evolutionary computation and neural networks. His publications have been well cited Googlescholar Citations: 11k. During the last decades, on one hand, it had been highlighted the duality between chaotic numbers and pseudo-random numbers e. On the other hand, emergence of pseudo-randomness from chaos via various under-sampling methods has been recently discovered. Because nowadays there exist increasing demands for new and more efficient number generators of this type these demands arise from different applications, such as multi-agents competition, global optimisation via evolutionary algorithms or secure information transmission ,etc.
Welcome to LiVE (Learning Intelligence & Vision Essential) Group
Mathematical chaotic circuits have been recently introduced for such a purpose. By analogy of electronic circuitry: i;e. Nowadays his research areas include complexity and emergences theories, dynamical systems, bifurcation and chaos, control of chaos and cryptography based chaos. Chua inventor of "Chua circuit" and Alexander Sharkovsky who introduced the "Sharkovsky's order".
The study of complex adaptive systems is among the key modern tasks in science. Such systems show radically different behaviours at different scales and in different environments, and mathematical modelling of such emergent behaviour is very difficult, even at the conceptual level. We require a new methodology to study and understand complex, emergent macroscopic phenomena. This talk will present the key ideas of the approach and will show how it can be applied to evolutionary dynamics.
His research interests include genetic programming, particle swarm optimisation, the theory of evolutionary algorithms, and brain-computer interfaces. He has been chair of numerous international conferences. He is an advisory board member of the Evolutionary Computation journal and an associate editor of the Genetic Programming and Evolvable Machines and of Swarm Intelligence journals. Originally, artificial neural networks were built from biologically inspired units called perceptrons.
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Later, other types of units became popular in neurocomputing due to their good mathematical properties. Among them, radial-basis-function RBF units and kernel units became most popular. The talk will discuss advantages and limitations of networks with these two types of computational units. Higher flexibility in choice of free parameters in RBF will be compared with benefits of geometrical properties of kernel models allowing applications of maximal margin classification algorithms, modelling of generalization in learning from data in terms of regularization, and characcterization of optimal solutions of learning tasks.
Critical influence of input dimension on behavior of these two types of networks will be described. General results will be illustrated by the paradigmatic examples of Gaussian kernel and radial networks. Her research interests include mathematical theory of neurocomputing and softcomputing, machine learning, and nonlinear approximation theory.
In she was awarded by the Czech Academy of Sciences the Bolzano Medal for her contributions to mathematics. The talk describes a new machine learning technique called Conformal Predictors. The advantages are as follows:. He chaired and participated in organising committees of many international conferences and workshops on Machine Learning and Bayesian methods in Europe, Russia and in the United States. Professor Gammerman's current research interest lies in field of Algorithmic Randomness Theory with its applications to machine learning and conformal predictors.
Areas in which these techniques have been applied include medical diagnosis, forensic science, genomics, proteomics and environment. Professor Gammerman has published over a hundred research papers and several books on computational learning and probabilistic reasoning. William B.
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Evolutionary computing, particularly genetic programming , can optimise software and software engineering, including evolving test benchmarks, search meta-heuristics, protocols, composing web services, improving hashing and garbage collection, redundant programming and even automatically fixing bugs. Often there are many potential ways to balance functionality with resource consumption. But a human programmer cannot try them all.
Also the optimal trade off may be different on each hardware platform and it vary over time or as usage changes. It may be genetic programming can automatically suggest different trade offs for each new market. Langdon worked in the power supply industries and as a software consultant before returning to university to gain a PhD on evolving software with genetic programming. Bill has worked both on application and theoretical foundations of GP and has written three books on GP and given presentations in five continents. Applications include scheduling, e-commerce, data mining, evolving combinations of classifiers MCS , swarm systems PSO and Bioinformatics eg non-human contaminants in the human genome databases.
Whilst theory includes GP schema theory, markov analysis, the halting probability and elementary fitness landscapes. Nowadays, nature-inspired metaheuristics have become an integrated part of soft computing and computational intelligence, and they have been applied to solve a wide range of tough optimization problems.
Seemingly simple algorithms can often deal with complex, even NP-hard, optimization problems with surprisingly good performance and results. In this talk, I will review some of the recent metaheuristic algorithms such as firefly algorithm and cuckoo search and their differences from particle swarm optimization and other metaheuristics. We will try to analyse the key components of metaheuristic methods in terms of convergence and search characteristics. We will also give a few examples in real-world applications and suggest some open problems for further research. He has authored a dozen books and published more than papers.
He is the Editor-in-Chief of Int. Differential Evolution DE is currently one of the most used population based stochastic metaheuristic. Its popularity is mainly due to its simplicity and effectiveness in solving various types of problems, including multi-objective, multi-modal, dynamic and constrained optimization problems. Since Rainer Storn and Kenneth Price proposed the first DE versions, more than fifteen years ago, dozens of differential evolution flavors, involving changes in the main operators, hybridization with other optimization methods, automated parameters tuning, self -adaptation schemes, structured populations and so on, have been proposed.
Despite the large number of reported applications of DE and of the huge volume of experimental results it is still difficult to give answers to questions like " Why is DE successful for a class of problems and why does it fail for other problems? The theoretical analysis of DE is still well behind the experimental results, most of the current knowledge on differential evolution being based on empirical observations.
This presentation will review the existing theoretical results concerning the convergence properties of DE and the influence of the choice of DE parameters on the population evolution and will focus on the usage of these results in deriving practical insights for designing effective and efficient optimization tools. Her main research interests are: evolutionary computing, machine learning, data mining, statistical modelling, image processing and high performance computing. The lecture contains a part of the essential and breakthrough opinions to the emergence and emergent phenomena till nowadays.
It introduces Complex Systems as one of the field appropriate for investigation of conditions of emergent phenomena initiation. Principal obstacles that obstruct a real investigation of emergent phenomena in the level of nowadays analytical science are introduced.
The cases of emergent phenomena in problem solving are illustrated. His CSc.