inf535 - Computational Intelligence I

inf535 - Computational Intelligence I

Department of Computing Science 6 KP
Module components Semester courses Winter semester 2024/2025 Examination
Lecture
  • Unlimited access 2.01.535 - Evolution Strategies Show lecturers
    • Jill Baumann
    • Prof. Dr. Oliver Kramer

    Dates on Monday, 17.02.2025 - Friday, 21.02.2025, Monday, 24.02.2025 08:00 - 18:00, Tuesday, 25.02.2025 10:00 - 12:00, Location: A04 2-221, A07 0-030 (Hörsaal G)
    The lecture on "Evolution Strategies" offers an in-depth exploration of optimization techniques that are pivotal in solving complex problems. It begins by introducing basic optimization concepts, setting the stage for more advanced strategies. The lecture delves into the (1+1)-ES, a simple evolution strategy that evolves solutions using one parent and one offspring per generation, illustrating the foundational mechanism of this approach. It further discusses the 1/5 success rule, a method for adapting the step size based on a target success rate, which helps maintain efficient progress. The concept of restarts is explored, emphasizing strategies to escape local optima and improve solution diversity. More complex is the (μ+λ)-ES, which involves multiple parents and offspring, enhancing the robustness and convergence rate of the strategy. Self-adaptation is highlighted as a crucial feature, allowing the algorithm to dynamically adjust its parameters to better suit the problem landscape. The lecture also covers the adaptation of the covariance matrix, a sophisticated technique that helps the algorithm learn and adapt to the shape of the optimization landscape. Experimental results are presented to showcase the practical applications and effectiveness of these strategies. Finally, benchmark functions described in the appendix serve as a standard for evaluating and comparing the performance of evolution strategies. In practical exercises, participants are introduced to Python and all algorithms are programmed to facilitate hands-on learning and application. The course is worth 6 ECTS.
Exercises
  • Unlimited access 2.01.535 - Evolution Strategies Show lecturers
    • Jill Baumann
    • Prof. Dr. Oliver Kramer

    Dates on Monday, 17.02.2025 - Friday, 21.02.2025, Monday, 24.02.2025 08:00 - 18:00, Tuesday, 25.02.2025 10:00 - 12:00, Location: A04 2-221, A07 0-030 (Hörsaal G)
    The lecture on "Evolution Strategies" offers an in-depth exploration of optimization techniques that are pivotal in solving complex problems. It begins by introducing basic optimization concepts, setting the stage for more advanced strategies. The lecture delves into the (1+1)-ES, a simple evolution strategy that evolves solutions using one parent and one offspring per generation, illustrating the foundational mechanism of this approach. It further discusses the 1/5 success rule, a method for adapting the step size based on a target success rate, which helps maintain efficient progress. The concept of restarts is explored, emphasizing strategies to escape local optima and improve solution diversity. More complex is the (μ+λ)-ES, which involves multiple parents and offspring, enhancing the robustness and convergence rate of the strategy. Self-adaptation is highlighted as a crucial feature, allowing the algorithm to dynamically adjust its parameters to better suit the problem landscape. The lecture also covers the adaptation of the covariance matrix, a sophisticated technique that helps the algorithm learn and adapt to the shape of the optimization landscape. Experimental results are presented to showcase the practical applications and effectiveness of these strategies. Finally, benchmark functions described in the appendix serve as a standard for evaluating and comparing the performance of evolution strategies. In practical exercises, participants are introduced to Python and all algorithms are programmed to facilitate hands-on learning and application. The course is worth 6 ECTS.
Notes on the module
Prerequisites

Basics of statistics
Prüfungszeiten

At the end of the lecture period
Module examination

Written or oral exam
Skills to be acquired in this module

After successful completion of the course, students should have acquired the ability to master the presented methods in theory and practice. The students should be able to recognize and model corresponding optimization and data analysis problems themselves and to apply the methods unerringly.

Professional competence
The students:

  • recognise optimisation problems
  • implement simple algorithms of heuristic optimisation
  • critically discuss solutions and selection of methods
  • deepen previous knowledge of analysis and linear algebra

Methodological competence
The students:

  • deepen programming skills
  • apply modelling skills
  • learn about the relation between problem class and method selection

Social competence
The students:

  • cooperatively implement content introduced in lecture
  • evaluate own solutions and compare them with those of their peers

Self-competence
The students:

  • evaluate own skills with reference to peers
  • realize personal limitations
  • adapt own problem solving approaches with reference to required method competences

Top