Studying an evolving complex system and drawing some conclusions from it is an integral part of nature-inspired computing;being a part of that complex system, some insight can also be gained from our knowledge of it. ...
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Studying an evolving complex system and drawing some conclusions from it is an integral part of nature-inspired computing;being a part of that complex system, some insight can also be gained from our knowledge of it. In this paper we study the evolution of the evolutionary computation co-authorship network using social network analysis tools, with the aim of extracting some conclusions on its mechanisms. In order to do this, we first examine the evolution of macroscopic properties of the EC co-authorship graph, and then we look at its community structure and its corresponding change along time. The EC network is shown to be in a strongly expansive phase, exhibiting distinctive growth patterns, both at the macroscopic and the mesoscopic level.
Analog circuits are one of the most important parts of modern electronic systems and the failure of electronic hardware presents a critical threat to the completion of modern aircraft, spacecraft, and robot missions. ...
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Analog circuits are one of the most important parts of modern electronic systems and the failure of electronic hardware presents a critical threat to the completion of modern aircraft, spacecraft, and robot missions. Compared to digital circuits, designing fault-tolerant analog circuits is a difficult and knowledge-intensive task. A simple but powerful method for robustness is a redundancy approach to use multiple circuits instead of single one. For example, if component failures occur, other redundant components can replace the functions of broken parts and the system can still work. However, there are several research issues to make the redundant system automatically. In this paper, we used evolutionary computation to generate multiple analog circuits automatically and then we combined the solutions to generate robust outputs. evolutionary computation is a natural way to produce multiple redundant solutions because it is a population-based search. Experimental results on the evolution of the low-pass, high-pass and band-stop filters show that the combination of multiple evolved analog circuits produces results that are more robust than those of the best single circuit. (c) 2011 Elsevier B.V. All rights reserved.
Bi-clustering of the gene expression data has become a special study in bioinformatics in recent years. In a gene expression data matrix a bi-cluster is a sub-matrix of genes and conditions that exhibits a high correl...
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Bi-clustering of the gene expression data has become a special study in bioinformatics in recent years. In a gene expression data matrix a bi-cluster is a sub-matrix of genes and conditions that exhibits a high correlation of expression activity across both rows and columns. The difficulty of finding significant bi-clusters in gene expression data grows exponentially with the size of the dataset. This proposed approach is based on evolutionary algorithm, which goal is to extract maximum similarity bi-clusters. In addition, the algorithm works for a special case, where the bi-clusters are approximately squares. We then extend the algorithm to handle various kinds of other cases. Experimental results show the effectiveness of the proposed approach.
As a popular technique for intensity-modulated radiotherapy, direct aperture optimization (DAO) aims at generating treatment plans for cancer cases without the relaxation of optimization models. Conventional DAO metho...
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As a popular technique for intensity-modulated radiotherapy, direct aperture optimization (DAO) aims at generating treatment plans for cancer cases without the relaxation of optimization models. Conventional DAO methods are mainly based on mathematical programming, which can quickly generate a single plan but is inefficient in offering multiple candidate plans for clinical doctors. Recently, metaheuristics have been employed by DAO to offer many plans at a time;however, they are criticized for showing low efficiency in the evaluation and repair of iteratively generated offspring solutions. To provide an efficient DAO method, this work proposes a multi-objective evolutionary algorithm with customized variation operators. These operators can not only generate promising plans but also ensure their validity, and thus the search efficiency is improved by the acceleration of convergence and the elimination of repair operations. The experimental results demonstrate that the proposed DAO method is superior over existing heuristics and metaheuristics in terms of both effectiveness and efficiency.
A Local Linear Embedding (LLE) module enhances the performance of two evolutionary computation (EC) algorithms employed as search tools in global optimization problems. The LLE employs the stochastic sampling of the d...
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A Local Linear Embedding (LLE) module enhances the performance of two evolutionary computation (EC) algorithms employed as search tools in global optimization problems. The LLE employs the stochastic sampling of the data space inherent in evolutionary computation in order to reconstruct an approximate mapping from the data space back into the parameter space. This allows to map the target data vector directly into the parameter space in order to obtain a rough estimate of the global optimum, which is then added to the EC generation. This process is iterated and considerably improves the EC convergence. Thirteen standard test functions and two real-world optimization problems serve to benchmark the performance of the method. In most of our tests, optimization aided by the LLE mapping outperforms standard implementations of a genetic algorithm and a particle swarm optimization. The number and ranges of functions we tested suggest that the proposed algorithm can be considered as a valid alternative to traditional EC tools in more general applications. The performance improvement in the early stage of the convergence also suggests that this hybrid implementation could be successful as an initial global search to select candidates for subsequent local optimization.
Using evolutionary computation, we empirically investigate convergence properties of Gaussian process based Bayesian optimization (BO). We use evolutionary computation for the learning of the prediction model and opti...
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Using evolutionary computation, we empirically investigate convergence properties of Gaussian process based Bayesian optimization (BO). We use evolutionary computation for the learning of the prediction model and optimization of the acquisition function (auxiliary search) of BO. For practical use for materials informatics, we address three issues in BO: (1) the stopping conditions, (2) the initial data size, and (3) the unknown smoothness of the target function. Then, we introduce a goal-directed acquisition function in which a target value as a desired property of a compound is incorporated. In addition, we present an ensemble method of BO, in which each BO in the ensemble has a random property and a kernel function with a different smoothness. Experimental results for the materials data sets on melting points of binary compounds and hydrogen weight percentages of hydrogen storage materials with two to four constituent elements show the effectiveness of the ensemble method of BO. Additionally, using an ensemble of BOs presents that the obtained results (increase of the number of samples acquired) are not simply a result of additional BOs. The goal-directed acquisition function and the ensemble of BOs which we propose should be techniques that can be used in the realization of a new materials recommendation system with a self-learning algorithm. Due to the self-learning algorithm realized by BO, the property prediction performance of the algorithm would be increasingly improved. (C) 2017 Elsevier Ltd. All rights reserved.
This paper introduces two new approaches to building sparse least square support vector machines (LSSVM) based on genetic algorithms (GAs) for classification tasks. LSSVM classifiers are an alternative to SVM ones bec...
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This paper introduces two new approaches to building sparse least square support vector machines (LSSVM) based on genetic algorithms (GAs) for classification tasks. LSSVM classifiers are an alternative to SVM ones because the training process of LSSVM classifiers only requires to solve a linear equation system instead of a quadratic programming optimization problem. However, the absence of sparseness in the Lagrange multiplier vector (i.e. the solution) is a significant problem for the effective use of these classifiers. In order to overcome this lack of sparseness, we propose both single and multi-objective GA approaches to leave a few support vectors out of the solution without affecting the classifier's accuracy and even improving it. The main idea is to leave out outliers, non-relevant patterns or those ones which can be corrupted with noise and thus prevent classifiers to achieve higher accuracies along with a reduced set of support vectors. Differently from previous works, genetic algorithms are used in this work to obtain sparseness not to find out the optimal values of the LSSVM hyper-parameters. (C) 2015 Elsevier B.V. All rights reserved.
The human brain functional connectivity network (FCN) is constrained and shaped by the communication processes in the structural connectivity network (SCN). The underlying communication mechanism thus becomes a critic...
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The human brain functional connectivity network (FCN) is constrained and shaped by the communication processes in the structural connectivity network (SCN). The underlying communication mechanism thus becomes a critical issue for understanding the formation and organization of the FCN. A number of communication models supported by different routing strategies have been proposed, with shortest path (SP), random diffusion (DIF), and spatial navigation (NAV) as the most typical, respectively requiring network global knowledge, local knowledge, and both for path seeking. Yet these models all assumed every brain region to use one routing strategy uniformly, ignoring convergent evidence that supports the regional heterogeneity in both terms of biological substrates and functional roles. In this regard, the current study developed a hybrid communication model that allowed each brain region to choose a routing strategy from SP, DIF, and NAV independently. A genetic algorithm was designed to uncover the underlying region-wise hybrid routing strategy (namely HYB). The HYB was found to outperform the three typical routing strategies in predicting FCN and facilitating robust communication. Analyses on HYB further revealed that brain regions in lower-order functional modules inclined to route signals using global knowledge, while those in higher-order functional modules preferred DIF that requires only local knowledge. Compared to regions that used global knowledge for routing, regions using DIF had denser structural connections, participated in more functional modules, but played a less dominant role within modules. Together, our findings further evidenced that hybrid routing underpins efficient SCN communication and locally heterogeneous structure-function coupling.
Meta-modeling has become a crucial tool in solving expensive optimization problems. Much of the work in the past has focused on finding a good regression method to model the fitness function. Examples include classica...
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Meta-modeling has become a crucial tool in solving expensive optimization problems. Much of the work in the past has focused on finding a good regression method to model the fitness function. Examples include classical linear regression, splines, neural networks, Kriging and support vector regression. This paper specifically draws attention to the fact that assessing model accuracy is a crucial aspect in the meta-modeling framework. Resampling strategies such as cross-validation, subsampling, bootstrapping, and nested resampling are prominent methods for model validation and are systematically discussed with respect to possible pitfalls, shortcomings, and specific features. A survey of meta-modeling techniques within evolutionary optimization is provided. In addition, practical examples illustrating some of the pitfalls associated with model selection and performance assessment are presented. Finally, recommendations are given for choosing a model validation technique for a particular setting.
We propose a simulation-based analog equivalence boundary search methodology for high level Simulink models and their low level HSpice counterparts. The equivalence of high and low level designs is determined by compa...
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We propose a simulation-based analog equivalence boundary search methodology for high level Simulink models and their low level HSpice counterparts. The equivalence of high and low level designs is determined by comparing a set of predefined performance parameters measured during the simulation of both models. Our methodology investigates the search space to obtain boundary of input parameters, where both models have equivalent performance parameters. We build an optimization problem, where the error percentage between the performance parameters of both models being less than a specified threshold is defined as success criteria. In this problem, input parameters are determined by utilizing evolutionary computation. At the end of the optimization, the border of equivalence for the models is found for input parameters satisfying the success criteria. We demonstrate the validity of our approach on three designs, an inverter, an operational amplifier, and a buck converter, where our approach proves to be an efficient tool in finding an equivalence boundary of analog circuits and models. (C) 2016 Elsevier B.V. All rights reserved.
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