Małgorzata Sterna. Mateusz Cicheński, Mateusz Jarus, Michał Miszkiewicz, Jarosław Szymczak
|
|
|
- Elvin Turner
- 6 years ago
- Views:
Transcription
1 Małgorzata Stera Mateusz Cicheński, Mateusz Jarus, Michał Miszkiewicz, Jarosław Szymczak Istitute of Computig Sciece Pozań Uiversity of Techology Pozań - Polad
2 Scope of the Talk Problem defiitio MP Formulatio Heuristic Algorithm Geetic Algorithm Computatioal Experimets Coclusios 1/20
3 Problem Defiitio web service for charitable orgaizatios gatherig charitable orgaizatios ad doors allowig submittig requests ad registerig offers for various products maagig database for registered users supportig supply process with optimizatio algorithms 2/20
4 Problem Defiitio charitable orgaizatio (customer) products are offered by depots (doors, warehouses, shops) i amout of a ij uits at price c ij uit trasportatio cost T demad for m types of products distace t ir i amout of d j uits positio of a depot i the route y ik amout of ordered uits x ij of particular products Malgorzata.Stera@cs.put.poza.pl 3/23
5 MP Formulatio mi x i1 m j1 ij c ij + T ( y i 1t 0i j1 i1 r1 1 k1 max{ 0,y y 1} t (y (1 y ))) ik r,k 1 ir ik i1 r1 r1 r,k 1 uder costraits i1 x ij x ij x ij a ij d j j 1...m i 1..., j 1...m 0 ad iteger i 1...,j order completio subproblem 1...m k1 k1 i1 i1 y ik order delivery subproblem y y y y ik ik ik i,k 1 mi{ 1, 1 1 {0,1} i1 m j1 x ij i 1... } k 1... y ik i 1... k i 1...,k Malgorzata.Stera@cs.put.poza.pl 4/20
6 Problem Formulatio selectig depots offerig demaded products at the lowest prices order completio easy problem greedy solutio is optimal determiig the shortest route to pick up products from these depots order delivery hard problem reduces to the shortest Hamiltoia cycle order completio ad delivery is strogly NP-hard as a variat of Travellig Purchaser Problem (Ramesh 1981) Malgorzata.Stera@cs.put.poza.pl 5/20
7 Heuristic Algorithm two-phase heuristic approach selectig depots costructig a tour Malgorzata.Stera@cs.put.poza.pl 6/20
8 Heuristic Algorithm selectig depots - choosig depots util demad is satisfied orderig depots accordig to product cost (greedy heuristic) weighted priorities (priority heuristic), based o: total distace to other locatios total cost of demaded products available at a depot total cost of all demaded products various priority weights result i various list heuristics ad various sets of selected depots Malgorzata.Stera@cs.put.poza.pl 7/20
9 Heuristic Algorithm costructig a tour from selected depots Miimum Spaig Tree Heuristic (Hedl & Karp 1970) costructig miimum spaig tree by Kruskal Algorithm traversig the tree accordig to Depth First Search Strategy covertig DFS sequece to the Hamiltoia cycle MSTH is 2-approximatio algorithm Malgorzata.Stera@cs.put.poza.pl 8/20
10 Bouds heuristic solutio determies upper boud (UB) lower boud LB= m ~ x, jc i i,j T( mi {t0i} j1 i1 i1... referece bouds RB= mi {t i0 }) m ~ x, jc i i,j T (mi{t0i} j1 i1 i1... i1... ~ t [k] mi {t i0 }) k1 i1... t [k] - k th distace betwee depots 1 Malgorzata.Stera@cs.put.poza.pl 9/20
11 Geetic Algorithm solutio is a sequece of assigmets: umber of product uits ordered from a depot oe product ca be take from more tha oe depot (to determie a complete solutio a tour is costructed by MST heuristic) iitial populatio heuristic solutios correspodig to various priorities weights radom solutios ew populatio replaces the previous oe Malgorzata.Stera@cs.put.poza.pl 10/20
12 Geetic Algorithm Operators oe-poit crossover ad two-poit crossover exchagig parts of assigmets product-depot ifeasible offsprig repair procedure: exceedig the product availability at a certai depot takig products from aother depot i offsprig takig products from a depot i paretal solutio mutatio replacig a give umber of assigmets (product-depot) with radom assigmet Malgorzata.Stera@cs.put.poza.pl 11/20
13 Geetic Algorithm - Selectio selectig matig populatio accordig to crossover rate roulette selectio (accordig to criterio values) rakig selectio (accordig to the positio i rakig) touramet selectio (the best solutio from radomly chose groups) mutatio accordig to mutatio rate Malgorzata.Stera@cs.put.poza.pl 12/20
14 Geetic Algorithm Termiatio Coditio the maximum umber of geeratios the maximum umber of geeratios without improvemet exceedig the satisfyig ratio of the criterio value improvemet 13/20
15 Computatioal Experimets radom istaces reflectig real world scearios depots located i 48 Polish cities the charitable orgaizatio located i Pozań distaces correspod to road distaces (Big Maps) uit trasportatio cost determied by govermet regulatios Malgorzata.Stera@cs.put.poza.pl 14/20
16 Computatioal Experimets a sigle order cotais of 5 to 200 product types (from 1 to 10 uits of each type) prices of products i depots are determied based o basic price modified by discout factor geerated with ormal distributio [-50%, +50%] Malgorzata.Stera@cs.put.poza.pl 15/20
17 Computatioal Experimets availability of products i depots are geerated accordig to 3 scearios: roud robi distributio demaded uits of a product are placed i depots oe by oe all depots have to be visited (order delivery is crucial) cloe distributio all demaded products are available i all depots prices of products are crucial (order completio is crucial) eve distributio availability i all depots is icreased by oe uit at the time util demad is exceeded prices ad distaces are crucial Malgorzata.Stera@cs.put.poza.pl 16/20
18 Time per geeratio 17/20
19 Trasportatio cost vs. Products cost 18/20
20 Criterio value vs. lower boud Roud Robi distributio Cloe distributio Eve distributio 19/20
21 Coclusios web service devoted for charitable istitutios data base of offered/doated products ad submitted requests optimizatio tool supportig realizig orders by miimizig products cost ad trasportatio cost solvig a variat of travellig purchaser problem by heuristic list algorithm geetic algorithm validatio algorithms i computatioal experimets solutio quality close to the lower boud short computatioal time acceptable by web service users Malgorzata.Stera@cs.put.poza.pl 20/20
An Improved Shuffled Frog-Leaping Algorithm for Knapsack Problem
A Improved Shuffled Frog-Leapig Algorithm for Kapsack Problem Zhoufag Li, Ya Zhou, ad Peg Cheg School of Iformatio Sciece ad Egieerig Hea Uiversity of Techology ZhegZhou, Chia lzhf1978@126.com Abstract.
condition w i B i S maximum u i
ecture 10 Dyamic Programmig 10.1 Kapsack Problem November 1, 2004 ecturer: Kamal Jai Notes: Tobias Holgers We are give a set of items U = {a 1, a 2,..., a }. Each item has a weight w i Z + ad a utility
Heuristic Approaches for Solving the Multidimensional Knapsack Problem (MKP)
Heuristic Approaches for Solvig the Multidimesioal Kapsack Problem (MKP) R. PARRA-HERNANDEZ N. DIMOPOULOS Departmet of Electrical ad Computer Eg. Uiversity of Victoria Victoria, B.C. CANADA Abstract: -
Optimization of Multiple Input Single Output Fuzzy Membership Functions Using Clonal Selection Algorithm
Optimizatio of Multiple Iput Sigle Output Fuzzy Membership Fuctios Usig Cloal Selectio Algorithm AYŞE MERVE ACILAR, AHMET ARSLAN Computer Egieerig Departmet Selcuk Uiversity Selcuk Uiversity, Eg.-Arch.
Algorithms for Disk Covering Problems with the Most Points
Algorithms for Disk Coverig Problems with the Most Poits Bi Xiao Departmet of Computig Hog Kog Polytechic Uiversity Hug Hom, Kowloo, Hog Kog csbxiao@comp.polyu.edu.hk Qigfeg Zhuge, Yi He, Zili Shao, Edwi
3D Model Retrieval Method Based on Sample Prediction
20 Iteratioal Coferece o Computer Commuicatio ad Maagemet Proc.of CSIT vol.5 (20) (20) IACSIT Press, Sigapore 3D Model Retrieval Method Based o Sample Predictio Qigche Zhag, Ya Tag* School of Computer
A Novel Approach to Solve Multiple Traveling Salesmen Problem by Genetic Algorithm
A Novel Approach to Solve Multiple Travelig Salesme Problem by Geetic Algorithm Adrás Király, Jáos Aboyi Uiversity of Paoia, Departmet of Process Egieerig, P.O. Box 58. Veszprém H-8200, HUNGARY, e-mail:
Lecture 6. Lecturer: Ronitt Rubinfeld Scribes: Chen Ziv, Eliav Buchnik, Ophir Arie, Jonathan Gradstein
068.670 Subliear Time Algorithms November, 0 Lecture 6 Lecturer: Roitt Rubifeld Scribes: Che Ziv, Eliav Buchik, Ophir Arie, Joatha Gradstei Lesso overview. Usig the oracle reductio framework for approximatig
Ones Assignment Method for Solving Traveling Salesman Problem
Joural of mathematics ad computer sciece 0 (0), 58-65 Oes Assigmet Method for Solvig Travelig Salesma Problem Hadi Basirzadeh Departmet of Mathematics, Shahid Chamra Uiversity, Ahvaz, Ira Article history:
Task scenarios Outline. Scenarios in Knowledge Extraction. Proposed Framework for Scenario to Design Diagram Transformation
6-0-0 Kowledge Trasformatio from Task Scearios to View-based Desig Diagrams Nima Dezhkam Kamra Sartipi {dezhka, sartipi}@mcmaster.ca Departmet of Computig ad Software McMaster Uiversity CANADA SEKE 08
SCHEDULING OPTIMIZATION IN CONSTRUCTION PROJECT BASED ON ANT COLONY GENETIC ALGORITHM
Joural of Theoretical ad Applied Iformatio Techology 8 th February 013. Vol. 48 No.3 005-013 JATIT & LLS. All rights reserved. ISSN: 199-8645 www.jatit.org E-ISSN: 1817-3195 SCHEDULING OPTIMIZATION IN
An Estimation of Distribution Algorithm for solving the Knapsack problem
Vol.4,No.5, 214 Published olie: May 25, 214 DOI: 1.7321/jscse.v4.5.1 A Estimatio of Distributio Algorithm for solvig the Kapsack problem 1 Ricardo Pérez, 2 S. Jös, 3 Arturo Herádez, 4 Carlos A. Ochoa *1,
Job Scheduling for Hybrid Assembly Differentiation Flowshop to Minimize Total Actual Flow Time
Job Schedulig for Hybrid Assembly Differetiatio Flowshop to Miimize Total Actual Flow Time Rahmi Maulidya, Rachmawati Wagsaputra, Suprayogi, Abdul Hakim Halim Faculty of Idustrial Techology Istitut Tekologi
UNIT 4 Section 8 Estimating Population Parameters using Confidence Intervals
UNIT 4 Sectio 8 Estimatig Populatio Parameters usig Cofidece Itervals To make ifereces about a populatio that caot be surveyed etirely, sample statistics ca be take from a SRS of the populatio ad used
Lecture Notes on Integer Linear Programming
Lecture Notes o Iteger Liear Programmig Roel va de Broek October 15, 2018 These otes supplemet the material o (iteger) liear programmig covered by the lectures i the course Algorithms for Decisio Support.
Introduction. Nature-Inspired Computing. Terminology. Problem Types. Constraint Satisfaction Problems - CSP. Free Optimization Problem - FOP
Nature-Ispired Computig Hadlig Costraits Dr. Şima Uyar September 2006 Itroductio may practical problems are costraied ot all combiatios of variable values represet valid solutios feasible solutios ifeasible
Lecture 17: Feature Subset Selection II
Lecture 17: Feature Subset Selectio II Expoetial search methods Brach ad Boud Approximate Mootoicity with Brach ad Boud Beam Search Radomized alorithms Radom Geeratio plus Sequetial Selectio Simulated
Assignment and Travelling Salesman Problems with Coefficients as LR Fuzzy Parameters
Iteratioal Joural of Applied Sciece ad Egieerig., 3: 557 Assigmet ad Travellig Salesma Problems with Coefficiets as Fuzzy Parameters Amit Kumar ad Aila Gupta * School of Mathematics ad Computer Applicatios,
CS 683: Advanced Design and Analysis of Algorithms
CS 683: Advaced Desig ad Aalysis of Algorithms Lecture 6, February 1, 2008 Lecturer: Joh Hopcroft Scribes: Shaomei Wu, Etha Feldma February 7, 2008 1 Threshold for k CNF Satisfiability I the previous lecture,
An Algorithm to Solve Fuzzy Trapezoidal Transshipment Problem
Iteratioal Joural of Systems Sciece ad Applied Mathematics 206; (4): 58-62 http://www.sciecepublishiggroup.com/j/ssam doi: 0.648/j.ssam.206004.4 A Algorithm to Solve Fuzzy Trapezoidal Trasshipmet Problem
Image Segmentation EEE 508
Image Segmetatio Objective: to determie (etract) object boudaries. It is a process of partitioig a image ito distict regios by groupig together eighborig piels based o some predefied similarity criterio.
Optimization of Multiple Traveling Salesmen Problem by a Novel Representation based Genetic Algorithm
Magyar Kutatók 0. Nemzetközi Szimpóziuma 0 th Iteratioal Symposium of Hugaria Researchers o Computatioal Itelligece ad Iformatics Optimizatio of Multiple Travelig Salesme Problem by a Novel Represetatio
Exact Minimum Lower Bound Algorithm for Traveling Salesman Problem
Exact Miimum Lower Boud Algorithm for Travelig Salesma Problem Mohamed Eleiche GeoTiba Systems mohamed.eleiche@gmail.com Abstract The miimum-travel-cost algorithm is a dyamic programmig algorithm to compute
Our second algorithm. Comp 135 Machine Learning Computer Science Tufts University. Decision Trees. Decision Trees. Decision Trees.
Comp 135 Machie Learig Computer Sciece Tufts Uiversity Fall 2017 Roi Khardo Some of these slides were adapted from previous slides by Carla Brodley Our secod algorithm Let s look at a simple dataset for
Pseudocode ( 1.1) Analysis of Algorithms. Primitive Operations. Pseudocode Details. Running Time ( 1.1) Estimating performance
Aalysis of Algorithms Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite amout of time. Pseudocode ( 1.1) High-level descriptio of a algorithm More structured
Copyright 2016 Ramez Elmasri and Shamkant B. Navathe
Copyright 2016 Ramez Elmasri ad Shamkat B. Navathe CHAPTER 19 Query Optimizatio Copyright 2016 Ramez Elmasri ad Shamkat B. Navathe Itroductio Query optimizatio Coducted by a query optimizer i a DBMS Goal:
Optimization on Retrieving Containers Based on Multi-phase Hybrid Dynamic Programming
Available olie at www.sciecedirect.com ScieceDirect Procedia - Social ad Behavioral Scie ce s 96 ( 2013 ) 844 855 Abstract 13th COTA Iteratioal Coferece of Trasportatio Professioals (CICTP 2013) Optimizatio
Multi Attributes Approach for Tourist Trips Design
Multi Attributes Approach for Tourist Trips Desig Ilaria Baffo 1, Pasquale Caroteuto 2, Atoella Petrillo 3, Fabio De Felice 4 1 Idustrial Egieerig School (DEIM) - Uiversity of Tuscia, Italy ilaria.baffo@uitus.it
An Efficient Algorithm for Graph Bisection of Triangularizations
A Efficiet Algorithm for Graph Bisectio of Triagularizatios Gerold Jäger Departmet of Computer Sciece Washigto Uiversity Campus Box 1045 Oe Brookigs Drive St. Louis, Missouri 63130-4899, USA jaegerg@cse.wustl.edu
Automatic Test Data Generation using Genetic Algorithm using Sequence Diagram
Iteratioal Joural of Computer Systems (ISSN: 2394-1065), Volume 03 Issue 02, February, 2016 Available at http://www.ijcsolie.com/ Automatic Test Data Geeratio usig Geetic Algorithm usig Sequece Diagram
Minimum Spanning Trees
Presetatio for use with the textbook, lgorithm esig ad pplicatios, by M. T. Goodrich ad R. Tamassia, Wiley, 0 Miimum Spaig Trees 0 Goodrich ad Tamassia Miimum Spaig Trees pplicatio: oectig a Network Suppose
It just came to me that I 8.2 GRAPHS AND CONVERGENCE
44 Chapter 8 Discrete Mathematics: Fuctios o the Set of Natural Numbers (a) Take several odd, positive itegers for a ad write out eough terms of the 3N sequece to reach a repeatig loop (b) Show that ot
Image Analysis. Segmentation by Fitting a Model
Image Aalysis Segmetatio by Fittig a Model Christophoros Nikou cikou@cs.uoi.gr Images take from: D. Forsyth ad J. Poce. Computer Visio: A Moder Approach, Pretice Hall, 2003. Computer Visio course by Svetlaa
Lower Bounds for Sorting
Liear Sortig Topics Covered: Lower Bouds for Sortig Coutig Sort Radix Sort Bucket Sort Lower Bouds for Sortig Compariso vs. o-compariso sortig Decisio tree model Worst case lower boud Compariso Sortig
Minimum Spanning Trees. Application: Connecting a Network
Miimum Spaig Tree // : Presetatio for use with the textbook, lgorithm esig ad pplicatios, by M. T. oodrich ad R. Tamassia, Wiley, Miimum Spaig Trees oodrich ad Tamassia Miimum Spaig Trees pplicatio: oectig
EE 459/500 HDL Based Digital Design with Programmable Logic. Lecture 13 Control and Sequencing: Hardwired and Microprogrammed Control
EE 459/500 HDL Based Digital Desig with Programmable Logic Lecture 13 Cotrol ad Sequecig: Hardwired ad Microprogrammed Cotrol Refereces: Chapter s 4,5 from textbook Chapter 7 of M.M. Mao ad C.R. Kime,
Greedy Algorithms. Interval Scheduling. Greedy Algorithms. Interval scheduling. Greedy Algorithms. Interval Scheduling
Greedy Algorithms Greedy Algorithms Witer Paul Beame Hard to defie exactly but ca give geeral properties Solutio is built i small steps Decisios o how to build the solutio are made to maximize some criterio
An Algorithm to Solve Multi-Objective Assignment. Problem Using Interactive Fuzzy. Goal Programming Approach
It. J. Cotemp. Math. Scieces, Vol. 6, 0, o. 34, 65-66 A Algorm to Solve Multi-Objective Assigmet Problem Usig Iteractive Fuzzy Goal Programmig Approach P. K. De ad Bharti Yadav Departmet of Mathematics
Fuzzy Rule Selection by Data Mining Criteria and Genetic Algorithms
Fuzzy Rule Selectio by Data Miig Criteria ad Geetic Algorithms Hisao Ishibuchi Dept. of Idustrial Egieerig Osaka Prefecture Uiversity 1-1 Gakue-cho, Sakai, Osaka 599-8531, JAPAN E-mail: hisaoi@ie.osakafu-u.ac.jp
OPTIMAL SEQUENCE OF HOLE-MAKING OPERATIONS USING PARTICLE SWARM OPTIMISATION AND SHUFFLED FROG LEAPING ALGORITHM
Egieerig Review, Vol. 36, Issue, 187-196, 016. 187 OPTIMAL SEQUENCE OF HOLE-MAKING OPERATIONS USING PARTICLE SWARM OPTIMISATION AND SHUFFLED FROG LEAPING ALGORITHM Amol M. Dalavi 1* - Padmakar J. Pawar
An Efficient Algorithm for Graph Bisection of Triangularizations
Applied Mathematical Scieces, Vol. 1, 2007, o. 25, 1203-1215 A Efficiet Algorithm for Graph Bisectio of Triagularizatios Gerold Jäger Departmet of Computer Sciece Washigto Uiversity Campus Box 1045, Oe
quality/quantity peak time/ratio
Semi-Heap ad Its Applicatios i Touramet Rakig Jie Wu Departmet of omputer Sciece ad Egieerig Florida Atlatic Uiversity oca Rato, FL 3343 jie@cse.fau.edu September, 00 . Itroductio ad Motivatio. relimiaries
6.854J / J Advanced Algorithms Fall 2008
MIT OpeCourseWare http://ocw.mit.edu 6.854J / 18.415J Advaced Algorithms Fall 2008 For iformatio about citig these materials or our Terms of Use, visit: http://ocw.mit.edu/terms. 18.415/6.854 Advaced Algorithms
Solving the Capacitated Vehicle Routing Problem Based on Improved Ant-clustering Algorithm
MATEC Web of Cofereces, 0 ( 05) DOI: 5/ mateccof/ 050 C Owed by the authors, published by EDP Scieces, 05 Solvig the Capacitated Vehicle Routig Problem Based o Improved At-clusterig Algorithm Jiasha Zhag*,
Graphs. Minimum Spanning Trees. Slides by Rose Hoberman (CMU)
Graphs Miimum Spaig Trees Slides by Rose Hoberma (CMU) Problem: Layig Telephoe Wire Cetral office 2 Wirig: Naïve Approach Cetral office Expesive! 3 Wirig: Better Approach Cetral office Miimize the total
An Integrated Matching and Partitioning Problem with Applications in Intermodal Transport
2015 IEEE Symposium Series o Computatioal Itelligece A Itegrated Matchig ad Partitioig Problem with Applicatios i Itermodal Trasport Erwi Pesch, Domiik Kress, Sebastia Meiswikel Uiversity of Siege Deptartmet
Heuristic Set-Covering-Based Postprocessing for Improving the Quine-McCluskey Method
Heuristic Set-Coverig-Based Postprocessig for Improvig the Quie-McCluskey Method Miloš Šeda Abstract Fidig the miimal logical fuctios has importat applicatios i the desig of logical circuits. This task
How do we evaluate algorithms?
F2 Readig referece: chapter 2 + slides Algorithm complexity Big O ad big Ω To calculate ruig time Aalysis of recursive Algorithms Next time: Litterature: slides mostly The first Algorithm desig methods:
Evolutionary Hybrid Genetic-Firefly Algorithm for Global Optimization
Iteratioal Joural of Computatioal Egieerig & Maagemet, Vol. 6 Issue, May ISSN (Olie): -789 www..org Evolutioary Hybrid Geetic-Firefly Algorithm for Global Optimizatio 7 Shaik Farook, P. Sagameswara Raju
Perturbation heuristics for the pickup and delivery traveling salesman problem
Computers & Operatios Research 29 (2002) 1129}1141 Perturbatio heuristics for the pickup ad delivery travelig salesma problem Jacques Reaud, Fayez F. Boctor, Gilbert Laporte * Te&le& -Uiversite&, 2600
Harris Corner Detection Algorithm at Sub-pixel Level and Its Application Yuanfeng Han a, Peijiang Chen b * and Tian Meng c
Iteratioal Coferece o Computatioal Sciece ad Egieerig (ICCSE 015) Harris Corer Detectio Algorithm at Sub-pixel Level ad Its Applicatio Yuafeg Ha a, Peijiag Che b * ad Tia Meg c School of Automobile, Liyi
Variance as a Stopping Criterion for Genetic Algorithms with Elitist Model
Fudameta Iformaticae 120 (2012) 145 164 145 DOI 10.3233/FI-2012-754 IOS Press Variace as a Stoppig Criterio for Geetic Algorithms with Elitist Model Diabadhu Bhadari, C. A. Murthy, Sakar K. Pal Ceter for
An Algorithm of Mobile Robot Node Location Based on Wireless Sensor Network
A Algorithm of Mobile Robot Node Locatio Based o Wireless Sesor Network https://doi.org/0.399/ijoe.v3i05.7044 Peg A Nigbo Uiversity of Techology, Zhejiag, Chia eirxvrp2269@26.com Abstract I the wireless
A New Approach To Scheduling Parallel Programs Using Task Duplication
A New Approach To Schedulig Parallel Programs Usig Task Duplicatio Ishfaq Ahmad ad Yu-Kwog Kwok Departmet of Computer Sciece Hog Kog Uiversity of Sciece ad Techology, Clear Water Bay, Kowloo, Hog Kog Abstract
Data Structures and Algorithms. Analysis of Algorithms
Data Structures ad Algorithms Aalysis of Algorithms Outlie Ruig time Pseudo-code Big-oh otatio Big-theta otatio Big-omega otatio Asymptotic algorithm aalysis Aalysis of Algorithms Iput Algorithm Output
Lecture 5. Counting Sort / Radix Sort
Lecture 5. Coutig Sort / Radix Sort T. H. Corme, C. E. Leiserso ad R. L. Rivest Itroductio to Algorithms, 3rd Editio, MIT Press, 2009 Sugkyukwa Uiversity Hyuseug Choo choo@skku.edu Copyright 2000-2018
Digital Microfluidic Biochips Online Fault Detection Route Optimization Scheme Chuan-pei XU and Tong-zhou ZHAO *
2018 Iteratioal Coferece o Commuicatio, Network ad Artificial Itelligece (CNAI 2018) ISBN: 978-1-60595-065-5 Digital Microfluidic Biochips Olie Fault Detectio Route Optimizatio Scheme Chua-pei XU ad Tog-zhou
Dynamic Programming and Curve Fitting Based Road Boundary Detection
Dyamic Programmig ad Curve Fittig Based Road Boudary Detectio SHYAM PRASAD ADHIKARI, HYONGSUK KIM, Divisio of Electroics ad Iformatio Egieerig Chobuk Natioal Uiversity 664-4 Ga Deokji-Dog Jeoju-City Jeobuk
Lecturers: Sanjam Garg and Prasad Raghavendra Feb 21, Midterm 1 Solutions
U.C. Berkeley CS170 : Algorithms Midterm 1 Solutios Lecturers: Sajam Garg ad Prasad Raghavedra Feb 1, 017 Midterm 1 Solutios 1. (4 poits) For the directed graph below, fid all the strogly coected compoets
Recursive Procedures. How can you model the relationship between consecutive terms of a sequence?
6. Recursive Procedures I Sectio 6.1, you used fuctio otatio to write a explicit formula to determie the value of ay term i a Sometimes it is easier to calculate oe term i a sequece usig the previous terms.
Modeling a Multi Depot K- Chinese Postman Problem with Consideration of Priorities for Servicing Arcs
Copyright 2015 America Scietific Publishers All rights reserved Prited i the Uited States of America Modelig a Multi Depot K- Chiese Postma Problem with Cosideratio of Priorities for Servicig Arcs Masoud
Continuous Ant Colony System and Tabu Search Algorithms Hybridized for Global Minimization of Continuous Multi-minima Functions
Cotiuous At Coloy System ad Tabu Search Algorithms Hybridized for Global Miimizatio of Cotiuous Multi-miima Fuctios Akbar Karimi Departmet of Aerospace Egieerig, Sharif Uiversity of Techology, P.O. Box:
Consider the following population data for the state of California. Year Population
Assigmets for Bradie Fall 2016 for Chapter 5 Assigmet sheet for Sectios 5.1, 5.3, 5.5, 5.6, 5.7, 5.8 Read Pages 341-349 Exercises for Sectio 5.1 Lagrage Iterpolatio #1, #4, #7, #13, #14 For #1 use MATLAB
The Impact of Feature Selection on Web Spam Detection
I.J. Itelliget Systems ad Applicatios, 2012, 9, 61-67 Published Olie August 2012 i MECS (http://www.mecs -press.org/) DOI: 10.5815/ijisa.2012.09.08 The Impact of Feature Selectio o Web Spam Detectio Jaber
Relay Placement Based on Divide-and-Conquer
Relay Placemet Based o Divide-ad-Coquer Ravabakhsh Akhlaghiia, Azadeh Kaviafar, ad Mohamad Javad Rostami, Member, IACSIT Abstract I this paper, we defie a relay placemet problem to cover a large umber
A Polynomial Interval Shortest-Route Algorithm for Acyclic Network
A Polyomial Iterval Shortest-Route Algorithm for Acyclic Network Hossai M Akter Key words: Iterval; iterval shortest-route problem; iterval algorithm; ucertaity Abstract A method ad algorithm is preseted
Informed Search. Russell and Norvig Chap. 3
Iformed Search Russell ad Norvig Chap. 3 Not all search directios are equally promisig Outlie Iformed: use problem-specific kowledge Add a sese of directio to search: work toward the goal Heuristic fuctios:
WEBSITE STRUCTURE IMPROVEMENT USING ANT COLONY TECHNIQUE
WEBSITE STRUCTURE IMPROVEMENT USING ANT COLONY TECHNIQUE Wiwik Aggraei 1, Agyl Ardi Rahmadi 1, Radityo Prasetyo Wibowo 1 1 Iformatio System Departmet, Faculty of Iformatio Techology, Istitut Tekologi Sepuluh
Accuracy Improvement in Camera Calibration
Accuracy Improvemet i Camera Calibratio FaJie L Qi Zag ad Reihard Klette CITR, Computer Sciece Departmet The Uiversity of Aucklad Tamaki Campus, Aucklad, New Zealad fli006, qza001@ec.aucklad.ac.z r.klette@aucklad.ac.z
Tutorial on Packet Time Metrics
Power Matters. Tutorial o Packet Time Metrics Lee Cosart lee.cosart@microsemi.com ITS 204 204 Microsemi Corporatio. COMPANY POPIETAY Itroductio requecy trasport Oe-way: forward & reverse packet streams
Abstract. Chapter 4 Computation. Overview 8/13/18. Bjarne Stroustrup Note:
Chapter 4 Computatio Bjare Stroustrup www.stroustrup.com/programmig Abstract Today, I ll preset the basics of computatio. I particular, we ll discuss expressios, how to iterate over a series of values
Analysis of Server Resource Consumption of Meteorological Satellite Application System Based on Contour Curve
Advaces i Computer, Sigals ad Systems (2018) 2: 19-25 Clausius Scietific Press, Caada Aalysis of Server Resource Cosumptio of Meteorological Satellite Applicatio System Based o Cotour Curve Xiagag Zhao
SOME NEW RESULTS ON THE TRAVELLING SALESMAN PROBLEM
Category: prelimiary commuicatio Domiika Crjac Milić 1 Ljiljaka Kvesić 2 SOME NEW RESULTS ON THE TRAVELLING SALESMAN PROBLEM Abstract: The travellig salesma problem (or The sales represetative problem)
OnePC: A portable software for parallel genetic algorithms used in optimization problems
OePC: A portable software for parallel geetic algorithms used i optimizatio problems Ioais G. Tsoulos, Departmet of Computer Egieerig, School of Applied Techology Techological educatioal istitute of Epirus,
Pattern Recognition Systems Lab 1 Least Mean Squares
Patter Recogitio Systems Lab 1 Least Mea Squares 1. Objectives This laboratory work itroduces the OpeCV-based framework used throughout the course. I this assigmet a lie is fitted to a set of poits usig
Mobile terminal 3D image reconstruction program development based on Android Lin Qinhua
Iteratioal Coferece o Automatio, Mechaical Cotrol ad Computatioal Egieerig (AMCCE 05) Mobile termial 3D image recostructio program developmet based o Adroid Li Qihua Sichua Iformatio Techology College
Lecture 13: Validation
Lecture 3: Validatio Resampli methods Holdout Cross Validatio Radom Subsampli -Fold Cross-Validatio Leave-oe-out The Bootstrap Bias ad variace estimatio Three-way data partitioi Itroductio to Patter Recoitio
Cubic Polynomial Curves with a Shape Parameter
roceedigs of the th WSEAS Iteratioal Coferece o Robotics Cotrol ad Maufacturig Techology Hagzhou Chia April -8 00 (pp5-70) Cubic olyomial Curves with a Shape arameter MO GUOLIANG ZHAO YANAN Iformatio ad
A New Bit Wise Technique for 3-Partitioning Algorithm
Special Issue of Iteratioal Joural of Computer Applicatios (0975 8887) o Optimizatio ad O-chip Commuicatio, No.1. Feb.2012, ww.ijcaolie.org A New Bit Wise Techique for 3-Partitioig Algorithm Rajumar Jai
1 Graph Sparsfication
CME 305: Discrete Mathematics ad Algorithms 1 Graph Sparsficatio I this sectio we discuss the approximatio of a graph G(V, E) by a sparse graph H(V, F ) o the same vertex set. I particular, we cosider
Choosing an Optimal Set of Libraries
IEEE TRANSACTIONS ON RELIABILITY, VOL. 45, NO. 2, 1996 JUNE Choosig a Optimal Set of Libraries Oded Berma Uiversity of Toroto, Toroto Michal Cutler, Member IEEE State Uiversity of New York, Bighamto Keywords
SOFTWARE usually does not work alone. It must have
Proceedigs of the 203 Federated Coferece o Computer Sciece ad Iformatio Systems pp. 343 348 A method for selectig eviromets for software compatibility testig Łukasz Pobereżik AGH Uiversity of Sciece ad
Using Simulated Annealing For Layout Planning of Construction Sites
Usig Simulated Aealig For Layout Plaig of Costructio Sites Mohamed El-Gafy, PhD, PE ad Tariq Abdelhamid, PhD Michiga State Uiversity East Lasig, MI Amie Ghaem PhD Califoria State Northridge Northridge,
COSC 1P03. Ch 7 Recursion. Introduction to Data Structures 8.1
COSC 1P03 Ch 7 Recursio Itroductio to Data Structures 8.1 COSC 1P03 Recursio Recursio I Mathematics factorial Fiboacci umbers defie ifiite set with fiite defiitio I Computer Sciece sytax rules fiite defiitio,
Scalable Exploration of Physical Database Design
Scalable Exploratio of Physical Database Desig Ard Christia Köig Microsoft Research chrisko@microsoft.com Shubha U. abar Staford Uiversity suabar@staford.edu Abstract Physical database desig is critical
Proceedings - 5th IMA Conference on Mathematics in Defence 23 November 2017, The Royal Military Academy Sandhurst, Camberley, UK
3 November 7, The Royal Military Academy Sadhurst, Camberley, UK Eteral Fake Costraits Iterpolatio: the ed of Ruge pheomeo with high degree polyomials relyig o equispaced odes Applicatio to aerial robotics
ISSN (Print) Research Article. *Corresponding author Nengfa Hu
Scholars Joural of Egieerig ad Techology (SJET) Sch. J. Eg. Tech., 2016; 4(5):249-253 Scholars Academic ad Scietific Publisher (A Iteratioal Publisher for Academic ad Scietific Resources) www.saspublisher.com
A METHOD OF GENERATING RULES FOR A KERNEL FUZZY CLASSIFIER
Proceedigs of the Sixth Iteratioal Coferece o Machie Learig ad Cyberetics, Hog Kog, 9- August 7 A METHOD OF GENERATING RULES FOR A KERNEL FUZZY CLASSIFIER AI-MIN YANG,, XIN-GUANG LI, LING-MIN JIANG,YONG-MEI
Perhaps the method will give that for every e > U f() > p - 3/+e There is o o-trivial upper boud for f() ad ot eve f() < Z - e. seems to be kow, where
ON MAXIMUM CHORDAL SUBGRAPH * Paul Erdos Mathematical Istitute of the Hugaria Academy of Scieces ad Reu Laskar Clemso Uiversity 1. Let G() deote a udirected graph, with vertices ad V(G) deote the vertex
Watermarking Integer Linear Programming Solutions
Watermarkig Iteger Liear Programmig Solutios Seapah Megeria Computer Sciece Departmet Uiversity of Califoria Los Ageles seapah@cs.ucla.edu Mileko Driić Computer Sciece Departmet Uiversity of Califoria
Research on K-Means Algorithm Based on Parallel Improving and Applying
Sed Orders for Reprits to reprits@bethamsciece.ae 288 The Ope Cyberetics & Systemics Joural, 2015, 9, 288-294 Ope Access Research o K-Meas Algorithm Based o Parallel Improvig ad Applyig Deg Zherog 1,2,*,
Normal Distributions
Normal Distributios Stacey Hacock Look at these three differet data sets Each histogram is overlaid with a curve : A B C A) Weights (g) of ewly bor lab rat pups B) Mea aual temperatures ( F ) i A Arbor,
SD vs. SD + One of the most important uses of sample statistics is to estimate the corresponding population parameters.
SD vs. SD + Oe of the most importat uses of sample statistics is to estimate the correspodig populatio parameters. The mea of a represetative sample is a good estimate of the mea of the populatio that
Implementing Dynamic Programming Recurrences in Constraint Handling Rules with Rule Priorities
Implemetig Dyamic Programmig Recurreces i Costrait Hadlig Rules with Rule Priorities Ahmed Magdy 1, Frak Raiser, ad Thom Frühwirth 1 Computer Sciece ad Egieerig, Germa Uiversity i Cairo ahmed.mabrouk@studet.guc.edu.eg
arxiv: v2 [cs.ds] 24 Mar 2018
![arxiv: v2 [cs.ds] 24 Mar 2018](/thumbs/81/83887444.jpg "arxiv: v2 [cs.ds] 24 Mar 2018")
Similar Elemets ad Metric Labelig o Complete Graphs arxiv:1803.08037v [cs.ds] 4 Mar 018 Pedro F. Felzeszwalb Brow Uiversity Providece, RI, USA pff@brow.edu March 8, 018 We cosider a problem that ivolves
ECE4050 Data Structures and Algorithms. Lecture 6: Searching
ECE4050 Data Structures ad Algorithms Lecture 6: Searchig 1 Search Give: Distict keys k 1, k 2,, k ad collectio L of records of the form (k 1, I 1 ), (k 2, I 2 ),, (k, I ) where I j is the iformatio associated
BASED ON ITERATIVE ERROR-CORRECTION
A COHPARISO OF CRYPTAALYTIC PRICIPLES BASED O ITERATIVE ERROR-CORRECTIO Miodrag J. MihaljeviC ad Jova Dj. GoliC Istitute of Applied Mathematics ad Electroics. Belgrade School of Electrical Egieerig. Uiversity
Load balanced Parallel Prime Number Generator with Sieve of Eratosthenes on Cluster Computers *
Load balaced Parallel Prime umber Geerator with Sieve of Eratosthees o luster omputers * Soowook Hwag*, Kyusik hug**, ad Dogseug Kim* *Departmet of Electrical Egieerig Korea Uiversity Seoul, -, Rep. of
Design Optimization Using Soft Computing Techniques For Extrusion Blow Molding Processes
Desig Optimizatio Usig Soft Computig Techiques For Extrusio Blow Moldig Processes Desig Optimizatio Usig Soft Computig Techiques For Extrusio Blow Moldig Processes Jyh-Cheg Yu*, Tsug-Re Hug ad Jyh-Yeog
Evaluation scheme for Tracking in AMI
A M I C o m m u i c a t i o A U G M E N T E D M U L T I - P A R T Y I N T E R A C T I O N http://www.amiproject.org/ Evaluatio scheme for Trackig i AMI S. Schreiber a D. Gatica-Perez b AMI WP4 Trackig: