- author = "Marco Gaudesi",
- title = "Advanced Techniques for Solving Optimization Problems through Evolutionary Algorithms",
- school = "DAUIN - Control and Computer Engineering, Politecnico di Torino",
- year = "2015",
- address = "Italy",
- month = feb,
- keywords = "genetic algorithms, genetic programming, muGP, microGP",
-
URL = "
http://porto.polito.it/2592954/",
-
URL = "
http://www.phd-dauin.polito.it/pdfs/Marco%20GAUDESI_thesis.pdf",
- size = "119 pages",
-
abstract = "Evolutionary algorithms (EAs) are machine-learning
techniques that can be exploited in several
applications in optimization problems in different
fields. Even though the first works on EAs appeared in
the scientific literature back in the 1960s, they
cannot be considered a mature technology, yet. Brand
new paradigms as well as improvements to existing ones
are continuously proposed by scholars and
practitioners. This thesis describes the activities
performed on uGP, an existing EA toolkit developed in
Politecnico di Torino since 2002. The works span from
the design and experimentation of new technologies, to
the application of the toolkit to specific industrial
problems.
More in detail, some studies addressed during these three years targeted: the realization of an optimal process to select genetic operators during the optimization process; the definition of a new distance metric able to calculate differences between individuals and maintaining diversity within the population (diversity preservation); the design and implementation of a new cooperative approach to the evolution able to group individuals in order to optimize a set of sub-optimal solutions instead of optimizing only one individual.",
-
notes = "Supervisor Giovanni Squillero
Ingegneria Informatica e dei Sistemi. COMPUTER SCIENCE AND SYSTEMS ENGINEERING",
