IPLUSO 24348

Stochastic Algorithms and Optimisation

• ApresentaçãoPresentation

  Optimization is necessary in all branches of knowledge. It is based on the search for efficiency. A good professional, particularly in IT, needs to have knowledge of optimization processes in order not only to be able to use them but also to advise potential users on the best algorithms, thus avoiding promoting poor optimization. Stochastic algorithms allow us to solve problems that would otherwise be difficult to solve or computationally expensive. It is therefore important to provide training in this area for future IT professionals.
• ProgramaProgramme

  CP 1 - Introduction to Stochastic Algorithms (Continuum problem and approximation via rational numbers; Deterministic Optimization; Simulated annealing; Variants; Complexity problems; Advantages and disadvantages of both approaches) CP2 - Genetic Algorithms CP3 - Bunch Algorithms (Ant Colony Optimization; Particle swarm optimization) CP4 - Stochastic Algorithms for Integration (Monte-Carlo Integration; Sampling) CP5 - Tracking Algorithms (Sequential Monte-Carlo or Particle Filter)
• ObjectivosObjectives

  LO1. Familiarization with optimization processes and some algorithms for that purpose, as well as a knowledge of the larg e families of stochastic algorithms and having some in-depth knowledge of some more significant algorithms. LO2. Understand not only the underlying ideas of the various algorithms but also the differences in terms of advantages and d isadvantages between them. LO3. Identify the fields of application of the various algorithms presented, as well as indicate how to code the various algorithms as a method of solving less complex problems. LO4. Understand and use libraries that have these algorithms already implemented
• BibliografiaBibliography

  Johannes Schneider, Scott Kirkpatrick -Stochastic Optimization (Scientific Computation),  Springer, 2007, ISBN-13: 978-3540345596 A.E. Eiben, J.E. Smith - Introduction to Evolutionary Computing, 2nd Edition, Springer, 2015, ISBN 978-3-662-44873-1 Ch. Venkateswarlu, Satya Eswari Jujjavarapu - Stochastic Global Optimization Methods and Applications to Chemical, Biochemical, Pharmaceutical and Environmental Processe,  Elsevier Science Publishing Co Inc, 2019,  ISBN13 9780128173923  
• MetodologiaMethodology

  After an initial theoretical presentation, which takes advantage of the practical contact component to create the conditions for students to be able to apply this component in the future, a phase begins where the theoretical component has practical applications that are presented and should be resolved in the contact hours of the practical part. As soon as the subject allows it, students are given a series of small problems that must be solved in practical classes and, if these are not enough, later independently. The minimum passing grade for the subject is 10 points. If the student does not pass the continuous assessment, a final exam is carried out, and the final classification will be the exam classification. Continuous assessment includes: 2 tests weighing 35% each and practical work weighing 30%.
• LínguaLanguage

  Português
• TipoType

  Semestral
• ECTS

  6
• NaturezaNature

  Mandatory
• EstágioInternship

  Não