Evolutionary Algorithms (EAs) are a set of optimization techniques that have become highly popular in recent decades. One of the main reasons for this success is that they provide a general purpose mechanism for solving a wide range of problems. Several approaches have been proposed, each of them having different search characteristics.
Hybrid Evolutionary Algorithms (HEAs) are an effective alternative when approaching the op- timization of a problem by means of EAs. The combination of several algorithms allows them to exploit the strength of each of the algorithms involved throughout the evolutionary process. Fur- thermore, it has been proven that, by means of the proper selection of algorithms and hybridization strategies, it is possible to obtain HEAs that outperform their composing algorithms thanks to the syn- ergic relationships yielded by the hybridization. This characteristic has been the main motivation for the studies that have been carried out in the development of this thesis. Each of these studies analyzes a different key factor in the combination of the algorithms with the aim of designing more efficient Hybrid Evolutionary Algorithms. These factors include the application of preliminary algorithms to conduct the initialization of the solutions, control mechanisms to manage the exchange of information in distributed models and adaptive hybridization strategies, just to introduce some of them briefly.
In the first study, a new initialization method is designed for the distributed Evolutionary Algorithms (dEAs). This mechanism uses a topological tool to restrict the initial search space of the nodes of the algorithm. The proposal carries out a systematic procedure by following two criteria: (i) homoge- neous coverage of the whole solution space, (ii) no overlap of the space explored by each island.
The behavior of the distributed Estimation of Distribution Algorithms (dEDAs) for continuous optimization is thoroughly analyzed as part of this thesis. The study infers the values of the param- eters that obtain the best performance in a selected competitive scenario, as well as the relationships between them. Special emphasis is placed on comparing the methods available for exchanging infor- mation: individuals or models.
In the third study, several competitive HEAs are defined and compared against the state-of-the-art algorithms in continuous optimization. These include a heterogeneous dEA, a memetic Differential Evolution (DE) algorithm and an adaptive High-level Relay Hybrid (HRH) algorithm. To design the adaptive algorithm, an extension to the Multiple Offspring Sampling (MOS) framework is conducted for defining HRH algorithms. All of the proposed algorithms achieve significant results, including some of the best results for the selected benchmarks.
The final objective of this thesis is to introduce a mechanism to learn how to control the combi- nation in HEAs. This task is achieved by a new framework that automatically generates competitive hybridization strategies in HRH algorithms. This procedure uses the information from several mea- sures of the algorithms in past executions to infer a new model that best characterizes the beneficial combination patterns.
To conclude, each of the proposals is tested on a set of well-known benchmarks on continuous optimization. Their results are compared with state of the art algorithms on continuous optimization by means of several statistical procedures.