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作者： Hitomi, Nozomi Bang, Hyunseung Selva, Daniel Cornell University IthacaNY14853 United States

As distributed satellite systems gain interest, there is a growing need for design tools that can identify system architectures with good trades in multiple metrics. evolutionary algorithms have shown promise as effective design-support tools but are computationally inefficient because they require evaluating many architectures before identifying ones with good tradeoffs. Knowledge-driven optimization aims to improve evolutionary algorithms by including a data-mining algorithm that learns design features common in the best architectures found during the optimization. These design features can guide the remainder of the optimization but should be used carefully to prevent misleading the search. This work presents a new knowledge-driven optimization framework, knowledge-driven optimization (KDO)/adaptive operator selection (AOS), that monitors how well the design features guide the search toward good architectures and focuses on using the best features. If none are beneficial to the optimization, KDO/AOS adapts its search strategy to explore regions of the trade space not captured by the design features. KDO/AOS is demonstrated on an architecture design problem for a distributed satellite system, and its adaptive search strategy enables it to discover high-quality architectures with over 800 fewer evaluations when compared with a conventional evolutionary algorithm. KDO/AOS is also more robust than other knowledge-driven optimization algorithms to inaccuracies from data mining. © 2018 American Institute of Aeronautics and Astronautics Inc.. All rights reserved.

关键词： evolutionary algorithms

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A novel memetic whale optimization algorithm for optimization 9th

A novel memetic whale optimization algorithm for optimizatio...

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9th International Conference on Swarm Intelligence, ICSI 2018

作者： Xu, Zhe Yu, Yang Yachi, Hanaki Ji, Junkai Todo, Yuki Gao, Shangce School of Computer Information and Engineering Changzhou Institute of Technology ChangzhouJiangsu213032 China Faculty of Engineering University of Toyama Toyama Japan Faculty of Electrical and Computer Engineering Kanazawa University Kanazawa-shi920-1192 Japan

ISBN: (纸本)9783319938141

Whale optimization algorithm (WOA) is a newly proposed search optimization technique which mimics the encircling prey and bubble-net attacking mechanisms of the whale. It has proven to be very competitive in comparison with other state-of-the-art metaheuristics. Nevertheless, the performance of WOA is limited by its monotonous search dynamics, i.e., only the encircling mechanism drives the search which mainly focus the exploration in the landscape. Thus, WOA lacks of the capacity of jumping out the of local optima. To address this problem, this paper propose a memetic whale optimization algorithm (MWOA) by incorporating a chaotic local search into WOA to enhance its exploitation ability. It is expected that MWOA can well balance the global exploration and local exploitation during the search process, thus achieving a better search performance. Forty eight benchmark functions are used to verify the efficiency of MWOA. Experimental results suggest that MWOA can perform better than its competitors in terms of the convergence speed and the solution accuracy. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： evolutionary algorithms

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Ramifications of Evolving Misbehaving Convolutional Neural Network Kernel and Batch Sizes

Ramifications of Evolving Misbehaving Convolutional Neural N...

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2018 IEEE/ACM Machine Learning in HPC Environments, MLHPC 2018

作者： Coletti, Mark Lunga, Dalton Berres, Anne Sanyal, Jibonananda Rose, Amy Geographic Information Science and Technology Group Oak Ridge National Laboratory Oak RidgeTN United States

ISBN: (纸本)9781728101804

Deep-learners have many hyper-parameters including learning rate, batch size, kernel size - all playing a significant role toward estimating high quality models. Discovering useful hyper-parameter guidelines is an active area of research, though the state of the art generally uses a brute force, uniform grid approach or random search for finding ideal settings. We share the preliminary results of using an alternative approach to deep learner hyper-parameter tuning that uses an evolutionary algorithm to improve the accuracy of a deep-learner models used in satellite imagery building footprint detection. We found that the kernel and batch size hyper-parameters surprisingly differed from sizes arrived at via a brute force uniform grid approach. These differences suggest a novel role for evolutionary algorithms in determining the number of convolution layers, as well as smaller batch sizes in improving deep-learner models. © 2018 IEEE.

关键词： evolutionary algorithms

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A historical interdependency based differential grouping algorithm for large scale global optimization 18

A historical interdependency based differential grouping alg...

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2018 Genetic and evolutionary Computation Conference, GECCO 2018

作者： Chen, An Ren, Zhigang Yang, Yang Liang, Yongsheng Pang, Bei Autocontrol Institute Xi'an Jiaotong University Xi'an Shaanxi710049 China Xi'an Jiaotong University Shenzhen Research School Shenzhen Guangdong518057 China

ISBN: (纸本)9781450357647

Cooperative co-evolution (CC) is a powerful evolutionary computation framework for solving large scale global optimization (LSGO) problems via the strategy of "divide-and-conquer", but its efficiency highly relies on the decomposition result. Existing decomposition algorithms either cannot obtain correct decomposition results or require a large number of fitness evaluations (FEs). To alleviate these limitations, this paper proposes a new decomposition algorithm named historical interdependency based differential grouping (HIDG). HIDG detects interdependency from the perspective of vectors. By utilizing historical interdependency information, it develops a novel criterion which can directly deduce the interdependencies among some vectors without consuming extra FEs. Coupled with an existing vector-based decomposition framework, HIDG further significantly reduces the total number of FEs for decomposition. Experiments on two sets of LSGO benchmark functions verified the effectiveness and efficiency of HIDG. © 2018 Association for Computing Machinery.

关键词： evolutionary algorithms

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A Reference-Inspired evolutionary Algorithm with Subregion Decomposition for Many-Objective Optimization

Advances in Intelligent Systems and Computing

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Advances in Intelligent Systems and Computing 2018年 650卷 145-156页

作者： Fu, Xiaogang Sun, Jianyong Zhang, Qingfu School of Electronic Engineering Shanghai Dianji University PuDong Shanghai201306 China School of Mathematics and Statistics Xi’an Jiaotong University Xi’an710049 China Department of Computer Science City University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9783319669380

In this paper, we propose a reference-inspired multiobjective evolutionary algorithm for many-objective optimisation. The main idea is (1) to summarise information inspired by a set of randomly generated reference points in the objective space to strengthen the selection pressure towards the Pareto front;and (2) to decompose the objective space into subregions for diversity management and offspring recombination. We showed that the mutual relationship between the objective vectors and the reference points provides not only a fine selection pressure, but also a balanced convergence-diversity information. The decomposition of the objective space into subregions is able to preserve the Pareto front’s diversity. A restricted stable match strategy is proposed to choose appropriate parent solutions from solution sets constructed at the subregions for high-quality offspring generation. Controlled experiments conducted on a commonly-used benchmark test suite have shown the effectiveness and competitiveness of the proposed algorithm in comparison with several state-of-the-art many-objective evolutionary algorithms. © 2018, Springer International Publishing AG.

关键词： evolutionary algorithms

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When is the immune inspired B-cell algorithm superior to the (1+1) evolutionary algorithm?

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International Journal of High Performance Computing and Networking 2018年第3期12卷 307-313页

作者： Xia, Xiaoyun Tang, Langping Peng, Xue College of Mathematics Physics and Information Engineering Jiaxing University Jiaxing Zhejiang314001 China School of Information Engineering Jiangxi University of Science and Technology Ganzhou China College of Medical Information Engineering Guangdong Pharmaceutical University Guangzhou China Department of Computer Science Guangdong Polytechnic Normal University Guangzhou China

There exist many experimental investigations of artificial immune systems (AIS), and it has been shown that the AIS is useful and efficient for many real-world optimisation problems. However, we know little about that whether the AIS can outperform the traditional evolutionary algorithms on some optimisation problems in theory. This work rigorously proved that a simple AIS called the B-cell algorithm (BCA) with somatic contiguous hypermutations can efficiently optimise two instances of the multiprocessor scheduling problem in expected polynomial runtime, whereas the local search algorithms and the (1+1) evolutionary algorithm ((1+1) EA) using only one individual in the search space and with standard bit mutation are highly inefficient. This work is helpful for gaining insight into the idea there exists no algorithm which is efficient for all specific problems. © 2018 Inderscience Enterprises Ltd.

关键词： evolutionary algorithms

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Identification of extreme wave heights with an evolutionary algorithm in combination with a likelihood-based segmentation

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PROGRESS IN ARTIFICIAL INTELLIGENCE 2017年第1期6卷 59-66页

作者： Duran-Rosal, Antonio M. Dorado-Moreno, Manuel Gutierrez, Pedro A. Hervas-Martinez, Cesar Univ Cordoba Dept Comp Sci & Numer Anal Rabanales CampusAlbert Einstein Bldg E-14071 Cordoba Spain

This paper presents four configurations of a genetic algorithm (GA) combined with a local search (LS) method for time series segmentation with the purpose of correctly recognising extreme values. The LS method is based on likelihood maximisation of a beta distribution. The proposal is tested on three real ocean wave height time series, where extreme values are frequently found. Concretely, the time series are taken from two oceanographic buoys in the Gulf of Alaska, and another one from Puerto Rico. The results show that the different combinations of LS improve the results of the GA. Furthermore, the algorithm provides segmentations where extreme values are grouped in a well-defined cluster, which allows the study of the characteristics of this type of events.

关键词： Time series segmentation evolutionary algorithms Extreme value distributions Clustering Likelihood-based optimisation Beta distribution

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evolutionary algorithm for structural-parametric optimization of the remote photoplethysmography method

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Optical Memory and Neural Networks (Information Optics) 2017年第1期26卷 55-61页

作者： Kopeliovich, M.V. Petrushan, M.V. Samarin, A.I. Institute for Mathematics Mechanics and Computer Science in the name of I.I. Vorovich Southern Federal University Rostov-on-Don Russia A.B. Kogan Research Institute for Neurocybernetics D.I. Ivanovsky Academy of Biology and Biotechnologies Southern Federal University Rostov-on-Don Russia

We analyzed a problem of determination of an optimal combination of algorithms and parameters of the remote photoplethysmography method for heart rate estimation. To solve this problem we developed an evolutionary algorithm implementing an adaptive reinforcement learning. The optimal solution of the problem of the remote photoplethysmography was obtained with the aid of an original criterion combining the mean square error with the dutation of agent’s life cycle. © 2017, Allerton Press, Inc.

关键词： evolutionary algorithms

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Performance of Traditional Production Scheduling Techniques Versus an evolutionary Algorithm

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Informacion Tecnologica 2018年第2期29卷 141-154页

作者： Castrillón, Omar D. Sarache, William Ruiz, Santiago Universidad Nacional de Colombia Sede Manizales Facultad de Ingeniería y Arquitectura Departamento de Ingeniería Industrial Campus la Nubia Manizales170001 Colombia

Comparison of some traditional techniques for the solution of production scheduling problems in Job Shop-Open Shop systems (FIFO, LIFO, SPT, LPT) with those of an evolutionary algorithm of the area of artificial intelligence is done. The results of an exhaustive analysis of 12600 production scheduling problems in Job Shop-Open Shop systems are presented. Although in most of the experiments the evolutionary algorithm showed a better performance in terms of the makespan, in some particular cases the use of traditional techniques were equally efficient. Despite the use of advanced techniques often provides better results for most production scheduling problem, this is not always true. © 2018 Centro de Informacion Tecnologica. All Rights Reserved.

关键词： evolutionary algorithms

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About the computational complexity of evolutionary algorithms with the elitist recording strategy

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ICIC Express Letters 2012年第7期6卷 1931-1937页

作者： Chen, Ming Ding, Lixin Yu, Jianping Zeng, Zhigao Xiao, Wei State Key Laboratory of Software Engineering Wuhan University Wuhan 430072 China College of Mathematics and Computer Science Hunan Normal University Lushan Road Changsha 410081 China

Among all theoretical issues of evolutionary algorithms, time complexity is one of the most important hot topics. Based on finite Markov chains, this paper provides an approach to estimating the average computation time of a special evolutionary algorithms. The elitist recording strategy is employed and the optimization problems are assumed to have multiple solutions. At first, we establish the Marov model of such evolutionary algorithms. The transition matrix differs from that under injective fitness function. Then, we derive bounds for the expected first hitting time on the optimal state set from the exact expression of convergence rates, and reveal the relationship between the bounds and norms of sub-matrices in evolutionary algorithms' transition matrix. Moreover, the convergence condition and parameters in bounds are simplified and concreted to be calculated. © 2012 ICIC International.

关键词： evolutionary algorithms

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