Heuristics and Metaheuristics for Optimization and Learning

• An introduction to Computational Theory and NP-complete problems
• An introduction to the base concepts in Machine Learning and Computational Learning Theory
• Landscape, Search Space and Optimization models
• Unconstrained optimization; constrained optimization; multiobjective optimization
  Optimization methods:
  o Algoritmi Greedy
  o Metodi Esatti: dynamic programming; A*; branch & bound algorithm; constraint programming
  o Meta-euristiche a singola soluzione: local search; tabu search; iterated local search; simulated annealing; guided local search; and GRASP
  o Meta-euristiche basate su popolazione: concetti base
   Metaheuristics population based:
  o Genetic Algorithms and Genetic Programming;
  o Artificial Immune Systems;
  o Swarm Intelligence: Ant Colony Optimization; Particle Swarm Optimization; Artificial Bee Colony;
  o Differential Evolution
   Hybird metaheuristics
   Multiobjective optimization evolutionary algorithms (MOEA)
   Metaheuristics in Decision Making
   Machine learning & Metaheuristics
   Examples of metaheuristics application in: Network Sciences; Games; Internet of Things; Computer Security; Robotics; Art and Design.

3. COURSE CONTENT.
 An introduction to Computational Theory and NP-complete problems
 An introduction to the base concepts in Machine Learning and Computational Learning Theory
 Landscape, Search Space and Optimization models
 Unconstrained optimization; constrained optimization; multiobjective optimization
 Optimization methods:
o Algoritmi Greedy
o Metodi Esatti: dynamic programming; A*; branch & bound algorithm; constraint programming
o Meta-euristiche a singola soluzione: local search; tabu search; iterated local search; simulated annealing; guided local search; and GRASP
o Meta-euristiche basate su popolazione: concetti base
 Metaheuristics population based:
o Genetic Algorithms and Genetic Programming;
o Artificial Immune Systems;
o Swarm Intelligence: Ant Colony Optimization; Particle Swarm Optimization; Artificial Bee Colony;
o Differential Evolution
 Hybird metaheuristics
 Multiobjective optimization evolutionary algorithms (MOEA)
 Metaheuristics in Decision Making
 Machine learning & Metaheuristics
 Examples of metaheuristics application in: Network Sciences; Games; Internet of Things; Computer Security; Robotics; Art and Design.

Timetable:

The course consists of two lessons (2h each) per week. Occasionally, there could be additional lessons, such as seminars, focusing on more practical aspects.

Timetable, start and end dates will be confirmed by the professor.

The course requires a good knowledge of mathematical tools (discrete and continuous); algorithms and data structures; as well as excellent knowledge of at least one of the following programming languages: C, C ++, and Python.

Study Level: Bachelor, Master, PhD

• Submit your application via the button ‘Apply Now’.
• Please, keep in mind that the number of participants could be limited for each course. Application does not guarantee enrolment in the course.
• The course participants will be selected based on criteria specified in the study guide.
• Your home university will inform you whether you have been accepted and provide further information about the next steps.