Natural Computing

Natural Computing

Cod: 22299

Department: DCET

Department: DCET

ECTS: 6

Scientific area: Computer Engineering

Scientific area: Computer Engineering

Total working hours: 156

Total contact time: 30

Total contact time: 30

The overall objective of this curricular unit is the study of so-called evolutionary computational techniques as well as others of natural and biological inspiration, aiming at promoting research and development skills in this area, developing in students a fundamental knowledge of evolutionary computational techniques and algorithms.

Upon completion of this course, the student should be able to:

1. Solve problems using the techniques studied;

2. Develop a critical spirit by comparing several methodologies;

3. Implement an evolutionary search and optimization algorithm.

1. Solve problems using the techniques studied;

2. Develop a critical spirit by comparing several methodologies;

3. Implement an evolutionary search and optimization algorithm.

1. Research and optimization methods: Exact methods; Classical Research Methods; Probabilistic Research Methods. Heuristic methods.

2. Evolutionary computing: Biological inspiration; Historical Perspective; Fundamental evolutionary algorithms.

3. Genetic Algorithms: Standard algorithm; Codifications, Fundamental operators; Advanced operators.

4. Algorithms based on Swarms and Ant Colonies: Optimization by Particle Swarm and Ant Colony.

5. Genetic Programming: Representation of solutions and fundamental operators.

6. Multiobjective Evolutionary Algorithms: Definition of a multiobjective optimization problem. Notions of dominance. Multiobjective Genetic Algorithms

2. Evolutionary computing: Biological inspiration; Historical Perspective; Fundamental evolutionary algorithms.

3. Genetic Algorithms: Standard algorithm; Codifications, Fundamental operators; Advanced operators.

4. Algorithms based on Swarms and Ant Colonies: Optimization by Particle Swarm and Ant Colony.

5. Genetic Programming: Representation of solutions and fundamental operators.

6. Multiobjective Evolutionary Algorithms: Definition of a multiobjective optimization problem. Notions of dominance. Multiobjective Genetic Algorithms

Deb, K. (2009). *MultiObjective Optimization Using Evolutionary Algorithms*, Wiley, ISBN13: 9780470743614.

Nunes de Castro L. (2006).*Fundamentals of Natural Computing*, Chapman & Hall, ISBN13: 9781584886433.

Michalewicz Z. e Fogel D.B. (2004).*How to Solve it: Modern Heuristics*, Springer, ISBN13: 9783540224945.

Nunes de Castro L. (2006).

Michalewicz Z. e Fogel D.B. (2004).

Evaluation is made on individual basis and it involves the coexistence of two modes: continuous assessment (60%) and
final evaluation (40%). Further information is detailed in the Learning Agreement of the course unit.