This is a repository of the material used for the PhD course of Laboratory of Population-based Optimisation Methods, held in May/June 2019 at Università degli Studi di Milano-Bicocca.
Instructors: Luca Manzoni, Luca Mariot, Marco S. Nobile
- Background on optimisation problems and single-state optimisation methods: gradient descent, hill-climbing, simulated annealing
- Genetic Algorithms
- Genetic Programming
- Evolution Strategies and Differential Evolution
- Swarm Intelligence: Particle Swarm Optimisation, Ant Colony Optimisation and Artificial Bee Colony Optimisation
- Representation for particular problems (lists, graph, rules)
- Distributed models (islands, master/slave)
- Multi-objective optimisation
- Implementation on HPC: advantages and common pitfalls
- Coevolution
- Neuroevolution
- Practical Application #1: parameter estimation in Systems Biology
- Practical Application #2: construction of Boolean functions for cryptography
- Runtime analysis and theory of evolutionary computation
All the references mentioned below are available on the Internet. Most of the books are freely available from the linked websites.
These books are proposed mainly for consultation purposes, if you want to deepen your understanding about a particular metaheuristic optimization method.
- Essentials of Metaheuristics
- A Field Guide to Genetic Programming
- Global Optimization Algorithms - Theory and Application
- Bioinspired Computation in Combinatorial Optimization
In this section, we gather surveys on the state of the art of metaheuristic/bio-inspired optimization, as well as position papers about where this research field should aim at.