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Advanced Techniques for Solving Optimization Problems through Evolutionary Algorithms

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 scientic 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 GP , 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 specic 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