Bandit-based cooperative coevolution for tackling contribution imbalance in large-scale optimization problems

This paper addresses the issue of computational resource allocation within the context of cooperative coevolution. Cooperative coevolution typically works by breaking a problem down into smaller subproblems (or components) and coevolving them in a round-robin fashion, resulting in a uniform resource allocation among its components. Despite its success on a wide range of problems, cooperative coevolution struggles to perform efficiently when its components do not contribute equally to the overall objective value. This is of crucial importance on large-scale optimization problems where such difference are further magnified. To resolve this imbalance problem, we extend the standard cooperative coevolution to a new generic framework capable of learning the contribution of each component using multi-armed bandit techniques. The new framework allocates the computational resources to each component proportional to their contributions towards improving the overall objective value. This approach results in a more economical use of the limited computational resources. We study different aspects of the proposed framework in the light of extensive experiments. Our empirical results confirm that even a simple bandit-based credit assignment scheme can significantly improve the performance of cooperative coevolution on large-scale continuous problems, leading to competitive performance as compared to the state-of-the-art algorithms

Hyper-Heuristic Image Enhancement (HHIE): A Reinforcement Learning Method for Image Contrast Enhancement

Conventional contrast enhancement methods such as Histogram Equalization (HE) have not acceptable results on many different low contrast images and they also cannot handle various images in automatically way. These problems result of specifying parameters manually in sake of producing high contrast images. We proposed an automatic image contrast enhancement on Hyper-Heuristic. In this study, simple exploiters are proposed to improve the contrast of current image. To select these exploiters appropriately, reinforcement learning is proposed. This selection is based on functional history of these exploiters. Having multi aim of preserving brightness, retaining the shape features of the original histogram and controlling on the rate of over-enhancement are the achievement of the proposed method. These objectives are suitable for the application of consumer electronics. By this simulation results, it has been shown that in terms of visual assessment, Peak Signal to Noise (PSNR) and Absolute Mean Brightness Error (AMBE). This study is superior to literature methods

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

LSU General Catalog 1990-1991

The LSU General Catalog describes all undergraduate and graduate departments and programs with degree requirements and courses offered for each one. The General Catalog includes information on registration and financial aid as well as academic services offered to all students

LSU General Catalog 1991-1992

The LSU General Catalog describes all undergraduate and graduate departments and programs with degree requirements and courses offered for each one. The General Catalog includes information on registration and financial aid as well as academic services offered to all students

College Catalog, 2006-2008, Graduate

https://digitalcommons.buffalostate.edu/buffstatecatalogs/1218/thumbnail.jp

College Catalog, 2008-2010, Graduate

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Animal Welfare Assessment

This Special Issue provides a collection of recent research and reviews that investigate many areas of welfare assessment, such as novel approaches and technologies used to evaluate the welfare of farmed, captive, or wild animals. Research in this Special Issue includes welfare assessment related to pilot whales, finishing pigs, commercial turkey flocks, and dairy goats; the use of sensors or wearable technologies, such as heart rate monitors to assess sleep in dairy cows, ear tag sensors, and machine learning to assess commercial pig behaviour; non-invasive measures, such as video monitoring of behaviour, computer vision to analyse video footage of red foxes, remote camera traps of free-roaming wild horses, infrared thermography of effort and sport recovery in sport horses; telomere length and regulatory genes as novel biomarkers of stress in broiler chickens; the effect of environment on growth physiology and behaviour of laboratory rare minnows and housing system on anxiety, stress, fear, and immune function of laying hens; and discussions of natural behaviour in farm animal welfare and maintaining health, welfare, and productivity of commercial pig herds

Annual Report of the University, 2005-2006, Volumes 1-7

PROPOSED POLICIES The Office of Government & Community Relations is in charge of advancing the University\u27s interests at all levels of federal, state and local government. The following policy guidelines for working with University units will achieve a coordinated and effective institutional advancement program. • To inform the Office of Government & Community Relations of all planned contacts and correspondence with elected officials and policy-making employees of federal, state and local government, including those who are alumni or friends of the University. Those items which pertain to sponsored research should be coordinated with the Vice President for Research. • To consult the Office of Government & Community Relations on any verbal or written statements made on behalf of the University that concern federal, state or local policies, legislation or regulations. • To advise the Office of Government & Community Relations on any activities, conferences, seminars, lectures or projects that involve the community and/or impact the University area. • Faculty or staff members who contact federal, state or local policy-making employees as experts in a specific field, or who act on behalf of themselves or another organization, should include a disclaimer which clearly states that they are not acting on behalf of the University

Catalog Denison University 2005-2006

Denison University Course Catalog 2005-2006https://digitalcommons.denison.edu/denisoncatalogs/1102/thumbnail.jp