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Adaptive α-stable differential evolution in numerical optimization (vol 16, pg 637, 2017)

NATURAL computing 2017年第4期16卷 659-659页

作者： Choi, Tae Jong Ahn, Chang Wook Sungkyunkwan Univ Dept Comp Engn 2066 Seobu Ro Suwon Gyeonggi Do South Korea GIST Sch Elect Engn & Comp Sci 123 Cheomdangwagi Ro Gwangju 61005 South Korea

Although Differential Evolution (DE) is a simple yet powerful evolutionary algorithm, it requires an adaptive parameter control to achieve its optimal performance. In this paper, DE with an adaptive parameter control using the \(\alpha\)-stable distribution is proposed. First, the proposed algorithm allocated a carefully calculated stable distribution, evaluated by an adaptation manner, to each individual. After that, each individual adjusts its own control parameters by using the assigned stable distribution. Thus, we propose a parameter control scheme that adapts the stability parameter of the \(\alpha\)-stable distribution to allocate proper stable distributions to each individual, used for tuning control parameters. We compared the optimization performances of the proposed algorithm with conventional DE and state-of-the-art DE variants at 30 and 100 dimensions of conventional benchmark problems. Also, we evaluated the optimization performances at high dimensional problems i.e., 100, 200, and 300 dimensions of CEC2008 benchmark problems. Our experiment results showed that the proposed algorithm is able to discover better final solutions than the compared DE algorithms and has the robust performance at both lower and higher dimensions.

关键词： Artificial intelligence evolutionary computing and genetic algorithms Global optimization

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Alignment using genetic programming with causal trees for identification of protein functions

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NONLINEAR ANALYSIS-THEORY METHODS & APPLICATIONS 2006年第5期65卷 1070-1093页

作者： Hung, Chun-Min Huang, Yueh-Min Chang, Ming-Shi Natl Cheng Kung Univ Dept Engn Sci Tainan 701 Taiwan Natl Cheng Kung Univ Dept Biochem Tainan 701 Taiwan

A hybrid evolutionary model is used to propose a hierarchical homology of protein sequences to identify protein functions systematically. The proposed model offers considerable potentials, considering the inconsistency of existing methods for predicting novel proteins. Because some novel proteins might align without meaningful conserved domains, maximizing the score of sequence alignment is not the best criterion for predicting protein functions. This work presents a decision model that can minimize the cost of making a decision for predicting protein functions using the hierarchical homologies. Particularly, the model has three characteristics: (i) it is a hybrid evolutionary model with multiple fitness functions that uses genetic programming to predict protein functions on a distantly related protein family, (ii) it incorporates modified robust point matching to accurately compare all feature points using the moment invariant and thin-plate spline theorems, and (iii) the hierarchical homologies holding up a novel protein sequence in the form of a causal tree can effectively demonstrate the relationship between proteins. This work describes the comparisons of nucleocapsid proteins from the putative polyprotein SARS virus and other coronaviruses in other hosts using the model. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： bioinformatics protein databases evolutionary computing and genetic algorithms splines invariants moments

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An Adaptive Population Resizing Scheme for Differential Evolution in Numerical Optimization

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JOURNAL OF COMPUTATIONAL AND THEORETICAL NANOSCIENCE 2015年第7期12卷 1336-1350页

作者： Choi, Tae Jong Ahn, Chang Wook Sungkyunkwan Univ Dept Comp Engn Suwon Gyeonggi Do South Korea

Applying the appropriate values to the control parameters of Differential Evolution (DE) is a challengeable problem. In order to enhance the optimization performance of DE, the exploration exploitation properties should be adjusted depending on the optimization progress. To make a balance between the exploration exploitation properties, the population size that is one of the control parameters can be controlled. In this paper, we proposed Adaptive Population Resizing (APR) scheme that calculates the deviation of the scattered individuals over search spaces in every certain evaluation counters and conducts adjusting the population size based on the information. Therefore, APR scheme can adjust the number of individuals by adding or subtracting some individuals depending on the optimization progress. We extended an adaptive differential evolution (SaDE) by attaching APR scheme and compared with several state-of-the-art DE algorithms in 14 classical benchmark problems. The experiment result showed that the proposed algorithm performed better than the compared DE algorithms in not only unimodal but also multimodal benchmark problems.

关键词： Artificial Intelligence evolutionary computing and genetic algorithms Global Optimization

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Evolving Bipartite Authentication Graph Partitions

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IEEE TRANSACTIONS ON DEPENDABLE AND SECURE computing 2019年第1期16卷 58-71页

作者： Pope, Aaron Tauritz, Daniel Kent, Alexander Missouri Univ Sci & Technol Dept Comp Sci Rolla MO 65409 USA Los Alamos Natl Lab Los Alamos NM 87545 USA

As large scale enterprise computer networks become more ubiquitous, finding the appropriate balance between user convenience and user access control is an increasingly challenging proposition. Suboptimal partitioning of users' access and available services contributes to the vulnerability of enterprise networks. Previous edge-cut partitioning methods unduly restrict users' access to network resources. This paper introduces a novel method of network partitioning superior to the current state-of-the-art which minimizes user impact by providing alternate avenues for access that reduce vulnerability. Networks are modeled as bipartite authentication access graphs and a multi-objective evolutionary algorithm is used to simultaneously minimize the size of large connected components while minimizing overall restrictions on network users. Results are presented on a real world data set that demonstrates the effectiveness of the introduced method compared to previous naive methods.

关键词： evolutionary computing and genetic algorithms network-level security and protection authentication graph theory

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Adaptive Human Action Recognition With an Evolving Bag of Key Poses

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IEEE TRANSACTIONS ON AUTONOMOUS MENTAL DEVELOPMENT 2014年第2期6卷 139-152页

作者： Andre Chaaraoui, Alexandros Florez-Revuelta, Francisco Univ Alicante Dept Comp Technol E-03080 Alicante Spain Univ Kingston Fac Sci Engn & Comp Kingston Upon Thames KT1 2EE Surrey England

Vision-based human action recognition allows to detect and understand meaningful human motion. This makes it possible to perform advanced human-computer interaction, among other applications. In dynamic environments, adaptive methods are required to support changing scenario characteristics. Specifically, in human-robot interaction, smooth interaction between humans and robots can only be performed if these are able to evolve and adapt to the changing nature of the scenarios. In this paper, an adaptive vision-based human action recognition method is proposed. By means of an evolutionary optimization method, adaptive and incremental learning of human actions is supported. Through an evolving bag of key poses, which models the learned actions over time, the current learning memory is developed to recognize increasingly more actions or actors. The evolutionary method selects the optimal subset of training instances, features and parameter values for each learning phase, and handles the evolution of the model. The experimentation shows that our proposal achieves to adapt to new actions or actors successfully, by rearranging the learned model. Stable and accurate results have been obtained on four publicly available RGB and RGB-D datasets, unveiling the method's robustness and applicability.

关键词： evolutionary computing and genetic algorithms feature evaluation and selection human computer interaction vision and scene understanding

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A systematic approach to the design of distributed wearable systems

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IEEE TRANSACTIONS ON COMPUTERS 2004年第8期53卷 1017-1033页

作者： Anliker, U Beutel, J Dyer, M Enzler, R Lukowicz, P Thiele, L Tröster, G ETH Zentrum Elect Lab CH-8092 Zurich Switzerland ETH Zentrum Comp Engn & Networks Lab CH-8092 Zurich Switzerland

Wearable computing has recently gained much popularity as an ambitious vision for future personalized mobile systems. Its aim is intelligent, environment aware systems unobtrusively embedded into the mobile environments of their users. With the combination of complex processing requirements, the necessity of placing sensors and input/output modules at different locations on the user's body, and stringent limits on size, weight, and battery capacity, the design of such systems is an inherently challenging problem. This paper demonstrates how systematic design and quantitative analysis can be applied to wearable architectures. We first present a model that allows various factors influencing the design of a wearable system to be incorporated into formal cost metrics. In particular, we show how to consistently incorporate specific wearable factors such as device placement requirements, ergonomics, and dynamic workload profiles into the model. We then discuss how efficient estimation algorithms can be extended and applied to the evaluation of different architectures with respect to our cost metrics. Finally, we discuss quantitative results from a proof-of-concept case study showing the trade offs between different architectures for a given wearable scenario. Summarized, this paper demonstrates how the description and the design of wearable systems can be put on a systematic, formal basis allowing us to treat them similar as conventional embedded systems.

关键词： wearable computers distributed architectures evolutionary computing and genetic algorithms modeling of computer architecture

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Distinguishing Endogenous Retroviral LTRs from SINE Elements Using Features Extracted from Evolved Side Effect Machines

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2012年第6期9卷 1676-1689页

作者： Ashlock, Wendy Datta, Suprakash York Univ Dept Comp Sci & Engn Toronto ON M3J 1P3 Canada

Side effect machines produce features for classifiers that distinguish different types of DNA sequences. They have the, as yet unexploited, potential to give insight into biological features of the sequences. We introduce several innovations to the production and use of side effect machine sequence features. We compare the results of using consensus sequences and genomic sequences for training classifiers and find that more accurate results can be obtained using genomic sequences. Surprisingly, we were even able to build a classifier that distinguished consensus sequences from genomic sequences with high accuracy, suggesting that consensus sequences are not always representative of their genomic counterparts. We apply our techniques to the problem of distinguishing two types of transposable elements, solo LTRs and SINEs. Identifying these sequences is important because they affect gene expression, genome structure, and genetic diversity, and they serve as genetic markers. They are of similar length, neither codes for protein, and both have many nearly identical copies throughout the genome. Being able to efficiently and automatically distinguish them will aid efforts to improve annotations of genomes. Our approach reveals structural characteristics of the sequences of potential interest to biologists.

关键词： Endogenous retroviruses LTR retrotransposons SINE elements feature evaluation and selection machine learning evolutionary computing and genetic algorithms side effect machines

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Fuzzy ARTMAP Prediction of Biological Activities for Potential HIV-1 Protease Inhibitors Using a Small Molecular Data Set

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2011年第1期8卷 80-93页

作者： Andonie, Razvan Fabry-Asztalos, Levente Abdul-Wahid, Christopher Badi' Abdul-Wahid, Sarah Barker, Grant I. Magill, Lukas C. Cent Washington Univ Dept Comp Sci Ellensburg WA 98926 USA Transylvania Univ Brasov Elect & Comp Dept Brasov Romania Cent Washington Univ Dept Chem Ellensburg WA 98926 USA

Obtaining satisfactory results with neural networks depends on the availability of large data samples. The use of small training sets generally reduces performance. Most classical Quantitative Structure-Activity Relationship (QSAR) studies for a specific enzyme system have been performed on small data sets. We focus on the neuro-fuzzy prediction of biological activities of HIV-1 protease inhibitory compounds when inferring from small training sets. We propose two computational intelligence prediction techniques which are suitable for small training sets, at the expense of some computational overhead. Both techniques are based on the FAMR model. The FAMR [1] is a Fuzzy ARTMAP (FAM) incremental learning system used for classification and probability estimation. During the learning phase, each sample pair is assigned a relevance factor proportional to the importance of that pair. The two proposed algorithms in this paper are: 1) The GA-FAMR algorithm, which is new, consists of two stages: a) During the first stage, we use a genetic algorithm (GA) to optimize the relevances assigned to the training data. This improves the generalization capability of the FAMR. b) In the second stage, we use the optimized relevances to train the FAMR. 2) The Ordered FAMR is derived from a known algorithm. Instead of optimizing relevances, it optimizes the order of data presentation using the algorithm of Dagher et al. [2], [3]. In our experiments, we compare these two algorithms with an algorithm not based on the FAM, the FS-GA-FNN introduced in [4], [5]. We conclude that when inferring from small training sets, both techniques are efficient, in terms of generalization capability and execution time. The computational overhead introduced is compensated by better accuracy. Finally, the proposed techniques are used to predict the biological activities of newly designed potential HIV-1 protease inhibitors.

关键词： Fuzzy neural networks evolutionary computing and genetic algorithms computational chemistry data mining

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A genetic Optimization Approach for Isolating Translational Efficiency Bias

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2011年第2期8卷 342-352页

作者： Raiford, Douglas W. Krane, Dan E. Doom, Travis E. W. Raymer, Michael L. Univ Montana Dept Comp Sci Missoula MT 59812 USA Wright State Univ Dept Biol Sci Dayton OH 45435 USA Wright State Univ Dept Comp Sci & Engn Dayton OH 45435 USA

The study of codon usage bias is an important research area that contributes to our understanding of molecular evolution, phylogenetic relationships, respiratory lifestyle, and other characteristics. Translational efficiency bias is perhaps the most well-studied codon usage bias, as it is frequently utilized to predict relative protein expression levels. We present a novel approach to isolating translational efficiency bias in microbial genomes. There are several existent methods for isolating translational efficiency bias. Previous approaches are susceptible to the confounding influences of other potentially dominant biases. Additionally, existing approaches to identifying translational efficiency bias generally require both genomic sequence information and prior knowledge of a set of highly expressed genes. This novel approach provides more accurate results from sequence information alone by resisting the confounding effects of other biases. We validate this increase in accuracy in isolating translational efficiency bias on 10 microbial genomes, five of which have proven particularly difficult for existing approaches due to the presence of strong confounding biases.

关键词： Codon usage bias evolutionary computing and genetic algorithms miscellaneous artificial intelligence computing methodologies GC-content strand bias translational efficiency

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Minimizing Fatigue Damage in Aircraft Structures

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IEEE INTELLIGENT SYSTEMS 2016年第4期31卷 22-29页

作者： Ruotsalainen, Marja Jylha, Juha Visa, Ari Tampere Univ Technol Ind Projects FIN-33101 Tampere Finland Tampere Univ Technol PhD Comp & Elect Engn FIN-33101 Tampere Finland Tampere Univ Technol Dept Signal Proc FIN-33101 Tampere Finland

Aircraft structural health monitoring refers to a process in which sensors assess structural state in terms of aging and deterioration. A knowledge discovery approach based on dynamic time warping and genetic programm... 详细信息

关键词： Aircraft Military aircraft Structural engineering Artificial intelligence Monitoring Aerospace electronics Wireless sensor networks INSPEC: Controlled Indexing socio-economic effects aerospace safety aircraft condition monitoring data mining fatigue genetic algorithms mechanical engineering computing INSPEC: Non-Controlled Indexing aircraft fleet fatigue damage minimization aircraft structures aircraft structural health monitoring SHM flight safety economic considerations knowledge discovery approach dynamic time warping genetic programming artificial intelligence Author Keywords evolutionary computing and genetic algorithms computing methodologies artificial intelligence intelligent systems applications and expert knowledge-intensive systems decision support machine learning pattern recognition engineering

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