PPT-Modelling and Multi-Objective Optimization of Quality Attri

Rafael Olaechea Steven Stewart Kryzstof Czarnecki Derek Rayside University of Waterloo 1 Customizing VariabilityRich Software Stakeholders need to customize software functionality to their specific needs. . as a . . Constrained . . Multi-Objective. Optimization. Monojit . Choudhury. Microsoft Research Lab, . India. monojitc@microsoft.com. A tale of the lazy tongue. Indo-Australia Workshop on Optimization in Human Language Technology. . Multi-objective. . Optimization. – A Big . Picture. Karthik. . Sindhya. , . PhD. Postdoctoral Researcher. Industrial Optimization Group. Department of Mathematical Information Technology. Karthik.sindhya@jyu.fi. Kalyan Shankar Bhattacharjee. Supervisor: Tapabrata Ray. Co-supervisor: Hemant Kumar Singh. Presentation overview. What class of problems do they represent and why is it important to solve them ?. Existing approaches and their limitations.  . L. . Robin Keller*, Jay Simon**. * . University of California, Irvine, . USA. President. , INFORMS (INFORMS.org). ** Defense Resources Management Institute, USA.  . 11TH . International Workshop on Operations . Optimization methods help us find solutions to problems where we seek to find the best of something.. This lecture is about how we formulate the problem mathematically.. In this lecture we make the assumption that we have choices and that we can attach numerical values to the ‘goodness’ of each alternative.. . Multiobjective. . Optimization. . Algorithms. Karthik. . Sindhya. , . PhD. Postdoctoral Researcher. Industrial Optimization Group. Department of Mathematical Information Technology. Karthik.sindhya@jyu.fi. NP-Hard. Conflicting objectives. Flow shop with both minimum . makespan. and tardiness objective. TSP problem with minimum distance, time and cost objective. Container management – balancing volume, weight and value. Vaddi. and Weng-Fai Wong . Multi-objective Precision Optimization. of DNNs for Edge Devices. Deep NN accelerator’s boom in recent years . Various approximation techniques applied. Edge devices : . The acronym PIDO stands for ProcessIntegration and Design Optimization. Infew words, a PIDO can be described as atool that allows the effectivemanagement of the design process andorienting it to the o (b) Fig. 4 Updating results 4. Conclusion This paper investigates FEMU consisting of multi-objective optimization and surrogate model. To validate the effectiveness of the proposed method, the ambient The Joint . Lectures. on . Evolutionary. . Algorithms. ,. Lecture. 1 - 11th of September 2021. Roy de Winter | . 1. Outline. Introduction. Ship Design Case. Related Work. SAMO-COBRA. Experiments. Panwadee Tangpattanakul, Nicolas Jozefowiez, Pierre Lopez. LAAS-CNRS. Toulouse, France. 6th Workshop on Computational Optimization (WCO'13). Kraków, Poland. 8 September 2013. Contents. Introduction. Joshua Carden. Shaun Deacon. Paul Kessler. Paul Speth. Evolving satellite market focuses on larger, more agile constellations. Traditional techniques for constellation design becomes intractable. Few tools exist to reduce solution space to a manageable number.  . L. . Robin Keller*, Jay Simon**. * . University of California, Irvine, . USA. President. , INFORMS (INFORMS.org). ** Defense Resources Management Institute, USA.  . 11TH . International Workshop on Operations .