34404148
Stochastic sketching: a new method for global optimization
Abstract In this paper, we propose Stochastic sketching for global optimization based on a simulation of human behaviour. Stochastic sketching tries to do things simply in the human way without too much interpretation instead of modeling the thought and strategies of human beings and applying an artificial model to problems. We introduce and discuss concepts and components essential to stochastic sketching in detail, including sampling guide, zooming controller, sketching model, precision threshold, and satisfaction probability. Experimental results of stochastic sketching on several test functions and a set of recommended parameter settings are given, as well as preliminary comparisons between stochastic sketching and related evolutionary algorithms including evolution strategies, evolutionary programming, and genetic algorithms.
14759214
Real-coded ECGA for economic dispatch
In this paper, we propose a new approach that consists of the extended compact genetic algorithm (ECGA) and split-on-demand (SoD), an adaptive discretization technique, to economic dispatch (ED) problems with nonsmooth cost functions. ECGA is designed for handling problems with decision variables of the discrete type, while the decision variables of ED problems are oftentimes real numbers. Thus, in order to employ ECGA to tackle ED problems, SoD is utilized for discretizing the continuous decision variables and works as the interface between ECGA and the ED problem. Furthermore, ED problems in practice are usually hard for traditional mathematical programming methodologies because of the equality and inequality constraints. Hence, in addition to integrating ECGA and SoD, in this study, we devise a repair operator specifically for making the infeasible solutions to satisfy the equality constraint. To examine the performance and effectiveness, we apply the proposed framework to two different-sized ED problems with nonsmooth cost function considering the valve-point effects. The experimental results are compared to those obtained by various evolutionary algorithms and demonstrate that handling ED problems with the proposed framework is a promising research direction.
14259579
FTXI: fault tolerance XCS in integer
In the realm of data mining, several key issues exists in the traditional classification algorithms, such as low readability, large rule number, and low accuracy with information losing. In this paper, we propose a new classification methodology, called fault tolerance XCS in integer (FTXI), by extending XCS to handle conditions in integers and integrating the mechanism of fault tolerance in the context of data mining into the framework of XCS. We also design and generate appropriate artificial data sets for examining and verifying the proposed method. Our experiments indicate that FTXI can provide the least rule number, obtain high prediction accuracy, and offer rule readability, compared to C4.5 and XCS in integer without fault tolerance.
393866
Adaptive discretization for probabilistic model building genetic algorithms
This paper proposes an adaptive discretization method, called Split-on-Demand (SoD), to enable the probabilistic model building genetic algorithm (PMBGA) to solve optimization problems in the continuous domain. The procedure, effect, and usage of SoD are described in detail. As an example, the integration of SoD and the extended compact genetic algorithm (ECGA), named real-coded ECGA (rECGA), is presented and numerically examined. The experimental results indicate that rECGA works well and SoD is effective. The behavior of SoD is analyzed and discussed, followed by the potential future work for SoD.
34645664
Applying Family Competition to Evolution Strategies for Constrained Optimization
This paper applies family competition to evolution strategies to solve constrained optimization problems. The family competition of Family Competition Evolution Strategy (FCES) can be viewed as a local competition involving the children generated from the same parent, while the selection is a global competition among all of the members in the population. According to our experimental results, the self-adaptation of strategy parameters with deterministic elitist selection may trap ESs into local optima when they are applied to heavy constrained optimization problems. By controlling strategy parameters with non-self adaptive rule, FCES can reduce the computation time of self-adaptive Gaussian mutation, diminish the complexity of selection from (m+1) to (m+m), and avoid to be premature. Therefore, FCES is capable of obtaining better performance and saving the computation time. In this paper, FCES is compared with other evolutionary algorithms on various benchmark problems and the results indicate that FCES is a powerful optimization technique.
2888098
Evolutionary interactive music composition
This paper proposes the CFE framework---Composition, Feedback, and Evolution---and presents an interactive music composition system. The system composes short, manageable pieces of music by interacting with users. The most important features of the system include creating customized music according to the user preference and providing the facilities specifically designed for producing large amounts of music. We present the structure as well as the implementation of the system and the auxiliary functionalities that enhance the system. We also introduce the auto-feedback test with which we verify and evaluate the interactive music composition system.
40831365
Mining Frequent Spatial Patterns in Image Databases
Mining useful patterns in image databases can not only reveal useful information to users but also help the task of data management. In this paper, we propose an image mining framework, Frequent Spatial Pattern mining in images (FSP), to mine frequent patterns located in a pair of spatial locations of images. A pattern in the FSP is associated with a pair of spatial locations and refers to the occurrence of the same image content in a set of images. This framework is designed to be general so as to accept different levels of representations of image content and different layout forms of spatial representations.
13128744
Bayesian Networks for Knowledge-Based Authentication
Knowledge-based authentication (KBA) has gained prominence as a user authentication method for electronic transactions. This paper presents a Bayesian network model of KBA grounded in probabilistic reasoning and information theory. The probabilistic semantics of the model parameters naturally lead to the definitions of two key KBA metrics-guessability and memorability. The statistical modeling approach allows parameter estimation using methods such as the maximum likelihood estimator (MLE). The information-theoretic view helps to derive the closed-form solutions to estimating the guessability and guessing entropy metrics. The results related to KBA metrics and the models under different attacking strategies and factoid distributions are unified under a game-theoretic framework that yields lower and upper bounds of optimal guessability. The paper also proposes a methodology for implementing a Bayesian network-based KBA system. Further, an empirical evaluation of the relative merits of two Bayesian network structures for KBA, the Naive Bayes (NB) and the Tree Augmented Naive Bayes (TAN), confirms the hypothesis that the TAN structure is superior in terms of authentication accuracy and error rates. The results of the theoretical analysis and the empirical study provide insights into the KBA design problem and establish a foundation for future research in the KBA area.
6104187
Binary analysis and automated hot patching for Web-based applications
Patching technologies are commonly applied to improve the dependability of software after release. This paper reports the design of an automated hot patching (AHP) framework that fully automates reasoning for the causes of failures and patching the binary code of Web-based applications. AHP admits the hardness for rooting out all faults before product release, and autonomously patches problems of application programs. By directly operating on binary code, AHP is universal to virtually all applications. A promising application of AHP is to shortcut a function of the remote maintenance center (RMC) and hence to reduce the turn around time for patches.
6316461
Partitioned optimization algorithms for multiple sequence alignment
Multiple sequence alignment is an important and difficult problem in molecular biology and bioinformatics. In this paper, we propose a partitioning approach that significantly improves the solution time and quality by utilizing the locality structure of the problem. The algorithm solves the multiple sequence alignment in three stages. First, an automated and suboptimal partitioning strategy is used to divide the set of sequences into several subsections. Then a multiple sequence alignment algorithm based on ant colony optimization is used to align the sequences of each subsection. Finally, the alignment of original sequences can be obtained by assembling the result of each subsection. The ant colony algorithm is highly optimized in order to avoid local optimal traps and converge to global optimal efficiently. Experimental results show that the algorithm can significantly reduce the running time and improve the solution quality on large-scale multiple sequence alignment benchmarks.
10299300
On the development of a fully automated cryosection image registration system
The CCD cryosection images for the male collected in the National Library of Medicine's Visible Human Project were registered through a procedure involving significant human intervention. In addition, several large misalignments were visible in the registered CCD data for the male. To expedite the exploitation of the visible human cryosection data sets, there is a need to develop a highly accurate yet efficient image registration tool. In this paper, we present the current status of the development of such a fully automated registration system. This system is based on the maximization of mutual information criterion. One key issue of this approach is a robust optimization scheme. The method developed in this paper is to estimate the initial search point through segmentations of regions of interest for each cryosection image slice. Experimental results using the 70 mm cryosection image data for the male are presented in this paper.