This paper contains a modern vision of the parallelization techniques used for evolutionary algorithms (EAs). The work is motivated by two fundamental facts: first, the different families of EAs have naturally converg...
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This paper contains a modern vision of the parallelization techniques used for evolutionary algorithms (EAs). The work is motivated by two fundamental facts: first, the different families of EAs have naturally converged in the last decade while parallel EAs (PEAS) seem still to lack unified studies, and second, there is a large number of improvements in these algorithms and in their parallelization that raise the need for a comprehensive survey. We stress the differences between the EA model and its parallel implementation throughout the paper. We discuss the advantages and drawbacks of PEAs. Also, successful applications are mentioned and open problems are identified. We propose potential solutions to these problems and classify the different ways in which recent results in theory and practice are helping to solve them. Finally, we provide a highly structured background relating PEAs in order to make researchers aware of the benefits of decentralizing and parallelizing an EA.
A framework for hybridizing evolutionary algorithms with the branch-and-bound algorithm (B&B) is presented in this paper. This framework is based on using B&B as an operator embedded in the evolutionary algori...
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A framework for hybridizing evolutionary algorithms with the branch-and-bound algorithm (B&B) is presented in this paper. This framework is based on using B&B as an operator embedded in the evolutionary algorithm. The resulting hybrid operator will intelligently explore the dynastic potential (possible children) of the solutions being recombined, providing the best combination of formae (generalized schemata) that can be constructed without introducing implicit mutation. As a basis for studying this operator, the general functioning of transmitting recombination is considered. Two important concepts are introduced, compatibility sets, and granularity of the representation. These concepts are studied in the context of different kinds of representation: orthogonal, non-orthogonal separable, and non-separable. The results of an extensive experimental evaluation are reported. It is shown that this model can be useful when problem knowledge is available in the form of an optimistic evaluation function. Scalability issues are also considered. A control mechanism is proposed to alleviate the increasing computational cost of the algorithm for highly multidimensional problems.
Equivalent electric circuit modeling of PV devices is widely used to predict PV electrical performance. The first task in using the model to calculate the electrical characteristics of a PV device is to find the model...
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Equivalent electric circuit modeling of PV devices is widely used to predict PV electrical performance. The first task in using the model to calculate the electrical characteristics of a PV device is to find the model parameters which represent the PV device. In the present work, parameter estimation for the model parameter using various evolutionary algorithms is presented and compared. The constraint set on the estimation process is that only the data directly available in module datasheets can be used for estimating the parameters. The electrical model accuracy using the estimated parameters is then compared to several electrical models reported in literature for various PV cell technologies. (C) 2013 Elsevier B.V. All rights reserved.
In this work, an efficient design of multiplier-less digital finite impulse response (FIR) filter is presented, where the sub-expression elimination (SE) algorithms are employed on filter coefficients, and optimizatio...
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In this work, an efficient design of multiplier-less digital finite impulse response (FIR) filter is presented, where the sub-expression elimination (SE) algorithms are employed on filter coefficients, and optimization is done with evolutionary algorithms. This FIR filter is designed with novelty of optimizing the quantized coefficients inside each of the respective optimization algorithm, instead of using two separate algorithms: one for generation of optimal continuous coefficients, and second for optimizing the quantized coefficients. Comparative analysis using different SE techniques have been utilized for reducing the requirement of adders on both binary represented and canonic signed digit converted filter coefficients. The simulation results illustrate the impact of proposed algorithm along with significant reduction in number of adders.
In this paper we introduce some techniques for the analysis of time complexity of evolutionary algorithms (EAs) based on a finite search space. The Markov property and the decomposition of a matrix are employed for th...
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In this paper we introduce some techniques for the analysis of time complexity of evolutionary algorithms (EAs) based on a finite search space. The Markov property and the decomposition of a matrix are employed for the exact analytic expressions of the mean first hitting times that EAs reach the optimal solutions (FHT-OS). Dynkin's formula, one-step increment analysis and some other probability methods are adopted for the lower and upper bounds of the mean FHT-OS. The theory of a non-negative matrix, including the Perron-Frobenius theorem, the Jordan standard form, etc., are applied for the convergence of EAs. In addition, some techniques for time complexity and convergence of general adaptive EAs are also involved in this paper. Finally, the theoretical results obtained in this paper are verified effectively by applying them to the analysis of a typical evolutionary algorithm, (1 + 1)-EA.
Recent research shows that orthogonal array based crossovers outperform standard and existing crossovers in evolutionary algorithms in solving parametrical problems with high dimensions and multi-optima. However, thos...
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Recent research shows that orthogonal array based crossovers outperform standard and existing crossovers in evolutionary algorithms in solving parametrical problems with high dimensions and multi-optima. However, those crossovers employed so far, ignore the consideration of interactions between genes. In this paper, we propose a method to improve the existing orthogonal array based crossovers by integrating information of interactions between genes. It is empirically shown that the proposed orthogonal array based crossover outperforms significantly both the existing orthogonal array based crossovers and standard crossovers on solving parametrical benchmark functions that interactions exist between variables. To further compare the proposed orthogonal array based crossover with the existing crossovers in evolutionary algorithms, a validation test based on car door design is used in which the effectiveness of the proposed orthogonal array based crossover is studied. (C) 2009 Elsevier Ltd. All rights reserved.
Biological networks are structurally adaptive and take on non-random topological properties that influence system robustness. Studies are only beginning to reveal how these structural features emerge, however the infl...
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Biological networks are structurally adaptive and take on non-random topological properties that influence system robustness. Studies are only beginning to reveal how these structural features emerge, however the influence of component fitness and community cohesion (modularity) have attracted interest from the scientific community. In this study, we apply these concepts to an evolutionary algorithm and allow its population to self-organize using information that the population receives as it moves over a fitness landscape. More precisely, we employ fitness and clustering based topological operators for guiding network structural dynamics, which in turn are guided by population changes taking place over evolutionary time. To investigate the effect on evolution, experiments are conducted on six engineering design problems and six artificial test functions and compared against cellular genetic algorithms and panmictic evolutionary algorithm designs. Our results suggest that a self-organizing topology evolutionary algorithm can exhibit robust search behavior with strong performance observed over short and long time scales. More generally, the coevolution between a population and its topology may constitute a promising new paradigm for designing adaptive search heuristics.
There is an increasing interest in the application of evolutionary algorithms (EAs) to induce classification rules. This hybrid approach can benefit areas where classical methods for rule induction have not been very ...
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There is an increasing interest in the application of evolutionary algorithms (EAs) to induce classification rules. This hybrid approach can benefit areas where classical methods for rule induction have not been very successful. One example is the induction of classification rules in imbalanced domains. Imbalanced data occur when one or more classes heavily outnumber other classes. Frequently, classical machine learning (ML) classifiers are not able to learn in the presence of imbalanced data sets, inducing classification models that always predict the most numerous classes. In this work, we propose a novel hybrid approach to deal with this problem. We create several balanced data sets with all minority class cases and a random sample of majority class cases. These balanced data sets are fed to classical ML systems that produce rule sets. The rule sets are combined creating a pool of rules and an EA is used to build a classifier from this pool of rules. This hybrid approach has some advantages over undersampling, since it reduces the amount of discarded information, and some advantages over oversampling, since it avoids overfitting. The proposed approach was experimentally analysed and the experimental results show an improvement in the classification performance measured as the area under the receiver operating characteristics (ROC) curve.
Over the last decade, a variety of evolutionary algorithms (EAs) have been proposed for solving multiobjective optimization problems. Especially more recent multiobjective evolutionary algorithms (MOEAs) have been sho...
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Over the last decade, a variety of evolutionary algorithms (EAs) have been proposed for solving multiobjective optimization problems. Especially more recent multiobjective evolutionary algorithms (MOEAs) have been shown to be efficient and superior to earlier approaches. In the development of new MOEAs, the strive is to obtain increasingly better performing MOEAs. An important question however is whether we can expect such improvements to converge onto a specific efficient MOEA that behaves best on a large variety of problems. The best MOEAs to date behave similarly or are individually preferable with respect to different performance indicators. In this paper, we argue that the development of new MOEAs cannot converge onto a single new most efficient MOEA because the performance of MOEAs shows characteristics of multiobjective problems. While we will point out the most important aspects for designing competent MOEAs in this paper, we will also indicate the inherent multiobjective tradeoff in multiobjective optimization between proximity and diversity preservation. We will discuss the impact of this tradeoff on the concepts and design of exploration and exploitation operators. We also present a general framework for competent MOEAs and show how current state-of-the-art MOEAs can be obtained by making choices within this framework. Furthermore, we show an example of how we can separate nondomination selection pressure from diversity preservation selection pressure and discuss the impact of changing the ratio between these components.
Subgroup discovery (SD) is a descriptive data mining technique using supervised learning. In this article, we review the use of evolutionary algorithms (EAs) for SD. In particular, we will focus on the suitability and...
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Subgroup discovery (SD) is a descriptive data mining technique using supervised learning. In this article, we review the use of evolutionary algorithms (EAs) for SD. In particular, we will focus on the suitability and potential of the search performed by EAs in the development of SD algorithms. Future directions in the use of EAs for SD are also presented in order to show the advantages and benefits that this search strategy contribute to this task. Conflict of interest: The authors have declared no conflicts of interest for this article. For further resources related to this article, please visit the .
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