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H02D1A Genetic Algorithms and Evolutionary Computing Dirk Roose

H02D1A Genetic Algorithms and Evolutionary Computing Dirk Roose. 1d semester; 4 credit points Aims to describe genetic algorithms and ( other ) evolutionary strategies for search and optimisation to analyse their performance ( quality of results , computational cost )

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H02D1A Genetic Algorithms and Evolutionary Computing Dirk Roose

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  1. H02D1AGeneticAlgorithmsandEvolutionaryComputingDirk Roose 1d semester; 4 credit points Aims • todescribegeneticalgorithmsand (other) evolutionarystrategiesfor search andoptimisation • to analyse theirperformance(quality of results, computationalcost) • todiscusssomeimplementation issues • toillustrate the methodsbysolvingsomemodel problems (e.g. travelling salesman problem, transportation problem) • to present somecase studies (e.g. concept learning, timetabling, ‘artificial life’) • the studentwillbeabletodecidewhether these methods are suitedtosolve a particular search or optimisationproblem, andhowtochoose the appropriatemethods / genetic operators

  2. GeneticAlgorithmsandEvolutionary Computing

  3. Prerequisite basic (bachelor) courses in informatics (programming, algorithms) andmathematics (analysis, statistics) • Course Material • some chapters from Genetic Algorithms and Genetic Programming. Modern Concepts and Practical Applications. M. Affenzeller, S. Winkler, S. Wagner, and A. Beham, Chapman and Hall/CRC 2009 (book ; e-book) • some papers • Teaching activities • lectures : 12 x 1.5 hour last lectures: short presentationsbystudentsaboutongoingproject (incl. interesting or unexpectedresults, questions) • exercises & practical sessions : 4 x 2.5h = 10 h • Project experiments with Matlab code, groups of 2 students, ± 40 hours • Exam open bookexam (theory & exercises) incl. discussion on project report

  4. GeneticAlgorithmsandEvolutionary Computing • Lectures: Wednesday9 am Celestijnenlaan 200D (Physics building), room 05.11 • Exercises: 2 groups; on Mondays & Tuesdays

  5. H03F9AParallel computingDirk Roose & Albert-Jan Yzelman 1d semester Aims Insight in • parallel computers andavailablesoftwareenvironments, • the design and performance analysis ofparallel algorithms. The student willbeableto • designefficient parallel versions of algorithmswithsimple data dependencies • both in the ‘shared addressspace’ programming model and in the ‘message passing’ programmingmodel. KU Leuven HPC Cluster with2736 ’cores’

  6. Revolution is Happening Now • Chip density is continuing to increase ~2x every 2 years • Clock speed is not • Number of processor cores may double instead • There is little or no hidden parallelism (ILP) to be found • Parallelism must be exposed to and managed by software Source: Intel, Microsoft (Sutter) and Stanford (Olukotun, Hammond)

  7. Parallel computing: Content • Architecture of parallel HPC systems (short) • Performance analysis on parallel systems • Design and analysis of parallel algorithmsfor model problems (matrix operations, sorting, fastFouriertransform) using theBSP model • Simple examples in BSPlib, BSPonMPI, MulticoreBSP (MPI: Message Passing Interface) • Dynamicload-balancing • Guestlecture on Parallel Matching by Rob Bisseling • …

  8. Parallel computing Prerequisites Bachelor-level knowledge of algorithms & programming Course material Rob H. Bisseling, Parallel Scientific Computation. A Structured Approach using BSP and MPI. Oxford University Press, 2004. + some papers Exercisesand practical sessions 5 sessions(3 on parallel systems: multicoreprocessor; HPC-cluster) no project Exam Open bookexam • insight in theory (in particular performance analysis) • design of anefficient parallel algorithm (high level description)

  9. Exascience Lab see www.exascience.com

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