Nature Inspired Artificial Intelligence

Recommended Prerequisites

Python Programming, Master AI Algorithms, Estatística, Readings and writing English skills. 

Teaching Methods

In the lectures we will present and discuss in a critical way the theories and methods used in heuristic problem solving. Immediately after the lecture students will exercise what was taught by solving in the computer medium complexity problems. This is a group work done under the supervision of the professor. Written synthesis of a recent research work, experimental work involving the statistical study of different alternatives for a theoretical question. Work subject to oral presentation and discussion.

Learning Outcomes

Present, discuss and develop computational methods for naturally inspired artificial intelligence solutions (i.e., biological, social, physical) for highly complex problems that either have no analytical solution or are computationally intractable.

Acquisition of skills to rigorously evaluate alternative solutions to problems.

Acquisition of skills in analysis and synthesis, oral and written communication (Portuguese and English), computer skills and statistical analysis, problem solving, knowledge of a foreign language, critical thinking, group work, autonomous learning, creativity, practical application of knowledge, research.

Work Placement(s)

No

Syllabus

1. Introduction

2. Classic Meta-Heuristics

  - Random Search

  - Hill Climbers

  - Simulated Annealing

3. Evolutionary Algorithms

  - The Evolutionary Algorithm

  - Representation

  - Initialisation

  - Parent Selection

  - Variation Operators

  - Survivor Selection

  - Population Control Mechanisms

4. Genetic Programming

  - Tree-Based

  - Graph-based

  - Linear

  - Strongly-Typed

  - Grammar-based

5. Evolutionary Strategies

  - Classical

  - CMA-ES

  - Natural ES

6. Diferential Evolution

7. Collective Intelligence

•          Ant Colony Optimization

•          Particle Swarm Optimization

8. Co-Evolution

  - Cooperation

  - Competition

9. Multi-Objective Optimization

10. Evolutionary Machine Learning

11. Design of Experiments and Evaluation

Assessment Methods

Assessment
Project: 50.0%
Exam: 50.0%

Bibliography

1) Introduction to Evolutionary Computation (2nd edition), A. Eiben and J. Smith, Springer, 2015.

2) Natural Computing Algorithms, Anthony Brabazon, Michael O’Neill and Seán McGarraghy, Springer, 2015.

3) Bio-Inspired Artificial Intelligence: theories, methods, and Technologies, Dario Floreano and Claudio Mattiussi, MIT Press, 2008.

4) Fundamentals of Natural Computing: basic concepts, algorithms, and applications, Leandro Castro, Chapman and Hall, 2006.

5) Essentials of Metaheuristics (2nd Edition), Sean Luke, Lulu Press, 2013.

6) Handbook of Evolutionary Machine Learning, Wolfgang Banzhaf, Penousal Machado, Mengjie Zhang, 2023.

7) Lectures on Intelligent Systems, Leonardo Vanneschi , Sara Silva, 2023.