Computational Intelligence for Optimization in Bioinformatics

Objectives

- Learning the basic concepts of optimization

- Understanding in-depth the functioning of the most used Computational Intelligence algorithm

- Implement those algorithms in Python and being able to use them to solve complex optimization problems.

General characterization

Code

12504

Credits

3.0

Responsible teacher

Leonardo Vanneschi

Hours

Weekly - 3

Total - 2

Teaching language

Português

Prerequisites

No previous knowledge required, unless basic concepts of Mathematics, Statistics and Computation, with specific reference to the Python programming language.

Bibliography

1. "Lectures on Intelligent Systems"

L. Vanneschi and S. Silva

Springer

2023

2.“Simulated Annealing and Boltzmann Machines”
E. Aarts and J. Korst
John Wiley and Sons

3.“Genetic Algorithms in Search, Optimization and Machine Learning”
D. E. Goldberg
Addison-Wesley

Teaching method

Theoretical classes are given using whiteboard and slides.

Practical classes are given using the PyCharm Python programming environment.

Evaluation method

The students will be evaluated thanks to the following criteria:

- Final exam: 70%

- Project: 30%

Subject matter

- Introduction to optimization and besic definitions.

- No Free Lunch Theorem

- Hill Climbing

- Fitness Landscapes. Local and Global Optima.

- Simulated Annealing

- Genetic Algorithms

- Advanced concepts of Genetic Algorithms

- Particle Swarm Optimization

Programs

Programs where the course is taught: