Advances in Hybrid Evolutionary Computation for Continuous Optimization

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

Benchmarking a MOS-based algorithm on the BBOB-2010 noiseless function testbed

In this contribution, a hybrid algorithm combining Differential Evolution and IPOP-CMA-ES is presented and benchmarked on the BBOB 2010 noiseless testbed. The hybrid algorithm has been constructed within the Multiple Offspring Sampling framework, which allows the seamless combination of multiple metaheuristics in a dynamic algorithm capable of adjusting the participation of each of the composing algorithms according to their current performance. The experimental results show a robust behavior of the algorithm and a good scalability as the dimensionality increases

A MOS-based Dynamic Memetic Differential Evolution Algorithm for Continuous Optimization: A Scalability Test

Continuous optimization is one of the areas with more activity in the field of heuristic optimization. Many algorithms have been proposed and compared on several benchmarks of functions, with different performance depending on the problems. For this reason, the combination of different search strategies seems desirable to obtain the best performance of each of these approaches. This contribution explores the use of a hybrid memetic algorithm based on the multiple offspring framework. The proposed algorithm combines the explorative/exploitative strength of two heuristic search methods that separately obtain very competitive results. This algorithm has been tested with the benchmark problems and conditions defined for the special issue of the Soft Computing Journal on Scalability of Evolutionary Algorithms and other Metaheuristics for Large Scale Continuous Optimization Problems. The proposed algorithm obtained the best results compared with both its composing algorithms and a set of reference algorithms that were proposed for the special issue

Automatically Modeling Hybrid Evolutionary Algorithms from Past Executions

ection of the most appropriate Evolutionary Algorithm for a given optimization problem is a difficult task. Hybrid Evolutionary Algorithms are a promising alternative to deal with this problem. By means of the combination of different heuristic optimization approaches, it is possible to profit from the benefits of the best approach, avoiding the limitations of the others. Nowadays, there is an active research in the design of dynamic or adaptive hybrid algorithms. However, little research has been done in the automatic learning of the best hybridization strategy. This paper proposes a mechanism to learn a strategy based on the analysis of the results from past executions. The proposed algorithm has been evaluated on a well-known benchmark on continuous optimization. The obtained results suggest that the proposed approach is able to learn very promising hybridization strategies

An Analysis of a Hybrid Evolutionary Algorithm by means of its Phylogenetic Information

The study conducted in this work analyses the interactions between different Evolutionary Algorithms when they are hybridized. For this purpose, the phylogenetic tree of the best solution reported by the hybrid algorithm is reconstructed, and the relationships among the ancestors of this solution are established. For each of these ancestors, the evolutionary techniques that generated that solution and the fitness increment introduced compared to its parents are recorded. The study reveals a structured interaction among the different evolutionary techniques that makes the hybrid algorithm to outperform each of its composing algorithms when executed individually. The Multiple Offspring Sampling framework has been used to develop the Hybrid EA studied in this work and the experiments have been conducted on the well-known CEC 2005 Benchmark for continuous optimizatio

Deviations of cup anemometer rotational speed measurements due to steady state harmonic accelerations of the rotor

The measurement deviations of cup anemometers are studied by analyzing the rotational speed of the rotor at steady state (constant wind speed). The differences of the measured rotational speed with respect to the averaged one based on complete turns of the rotor are produced by the harmonic terms of the rotational speed. Cup anemometer sampling periods include a certain number of complete turns of the rotor, plus one incomplete turn, the residuals from the harmonic terms integration within that incomplete turn (as part of the averaging process) being responsible for the mentioned deviations. The errors on the rotational speed due to the harmonic terms are studied analytically and then experimentally, with data from more than 500 calibrations performed on commercial anemometers

Distributed Estimation of Distribution Algorithms for continuous optimization: how does the exchanged information influence their behavior?

One of the most promising areas in which probabilistic graphical models have shown an incipient activity is the field of heuristic optimization and, in particular, in Estimation of Distribution Algorithms. Due to their inherent parallelism, different research lines have been studied trying to improve Estimation of Distribution Algorithms from the point of view of execution time and/or accuracy. Among these proposals, we focus on the so-called distributed or island-based models. This approach defines several islands (algorithms instances) running independently and exchanging information with a given frequency. The information sent by the islands can be either a set of individuals or a probabilistic model. This paper presents a comparative study for a distributed univariate Estimation of Distribution Algorithm and a multivariate version, paying special attention to the comparison of two alternative methods for exchanging information, over a wide set of parameters and problems ? the standard benchmark developed for the IEEE Workshop on Evolutionary Algorithms and other Metaheuristics for Continuous Optimization Problems of the ISDA 2009 Conference. Several analyses from different points of view have been conducted to analyze both the influence of the parameters and the relationships between them including a characterization of the configurations according to their behavior on the proposed benchmark