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JOURNAL OF MECHANICAL DESIGN 2021年第11期143卷 111703-111703页

作者： Planas, Robert Oune, Nick Bostanabad, Ramin Univ Calif Irvine Dept Mech & Aerosp Engn Irvine CA 92697 USA

Emulation plays an important role in engineering design. However, most emulators such as Gaussian processes (GPs) are exclusively developed for interpolation/regression and their performance significantly deteriorates in extrapolation. To address this shortcoming, we introduce evolutionary Gaussian processes (EGPs) that aim to increase the extrapolation capabilities of GPs. An EGP differs from a GP in that its training involves automatic discovery of some free-form symbolic bases that explain the data reasonably well. In our approach, this automatic discovery is achieved via evolutionary programming (EP) which is integrated with GP modeling via maximum likelihood estimation, bootstrap sampling, and singular value decomposition. As we demonstrate via examples that include a host of analytical functions as well as an engineering problem on materials modeling, EGP can improve the performance of ordinary GPs in terms of not only extrapolation, but also interpolation/regression and numerical stability.

关键词： Gaussian processes evolutionary programming extrapolation optimization

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A Protein Structure Prediction Program Architecture Based on a Software Transactional Memory 19

A Protein Structure Prediction Program Architecture Based on...

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6th Conference on the Engineering of Computer Based Systems (ECBS)

作者： Kordic, Branislav Popovic, Marko Popovic, Miroslav Goldstein, Moshe Amitay, Moshe Dayan, David Univ Novi Sad Fac Tech Sci Trg Dositeja Obradovica 6 Novi Sad Serbia Jerusalem Coll Technol Dept Comp Sci IL-9372115 Jerusalem Israel Jerusalem Coll Technol Dept Bioinformat IL-9372115 Jerusalem Israel

ISBN: (纸本)9781450376365

As an alternative for traditional lock-based synchronization mechanisms Software Transactional Memories (STMs) are dominantly evaluated on synthetic benchmarks and simplified applications rather than on real-world applications. So far, only a few notable examples for C++ and JAVA languages have been published. In this paper, an STM-based architecture of a Protein Structure Prediction (PSP) program for the Python language is presented. This STM-based architecture aims both, to enhance the existing barrier-based process synchronization implemented in the original version of that PSP program, and to provide the transactional-memory-based means for its future upgrade. The analysis of the performance metrics, such as the system execution time and the system scalability, is given too. The PSP program used here is DEEPSAM (Diffusion Equation evolutionary programming Simulated Annealing Method) which is implemented in the Python and Fortran programing languages. The key component which supports transactional execution is our PSTM (Python Software Transactional Memory) Python framework. The experimental results are evaluated against two peptides, namely enkephalin and 2mq5. The preliminary results show that the new PSTM-based version of DEEPSAM has comparable execution time relative to the original version, and that its architecture scales very well. Also, the results of this study did not reveal any architectural bottleneck. Considering that the original version of DEEPSAM already execute computation in parallel, gaining significant improvements regarding execution times was not expected. In order to comprehend PSTM's impact on a complex package such as DEEPSAM, regarding execution times, it must be run on a many-core processor capable of running dozens processes in parallel.

关键词： Software Transactional Memory Python DEEPSAM PSTM evolutionary programming

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Adaptive Gene Level Mutation

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ALGORITHMS 2021年第1期14卷 16页

作者： Al-Afandi, Jalal Horvath, Andras Peter Pazmany Catholic Univ Fac Informat Technol & Bion H-1083 Budapest Hungary Prater U 50-A H-1083 Budapest Hungary

Genetic Algorithms are stochastic optimization methods where solution candidates, complying to a specific problem representation, are evaluated according to a predefined fitness function. These approaches can provide solutions in various tasks even, where analytic solutions can not be or are too complex to be computed. In this paper we will show, how certain set of problems are partially solvable allowing us to grade segments of a solution individually, which results local and individual tuning of mutation parameters for genes. We will demonstrate the efficiency of our method on the N-Queens and travelling salesman problems where we can demonstrate that our approach always results faster convergence and in most cases a lower error than the traditional approach.

关键词： genetic algorithms evolution strategies adaptive mutation genetic programming evolutionary programming

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A New Maximum Weight Submatrix Solver Tool for Identifying Mutated Driver Pathways in Cancer using Python 10

A New Maximum Weight Submatrix Solver Tool for Identifying M...

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10th Annual Computing and Communication Workshop and Conference (CCWC)

作者： Kharroubi, Fouad El Kati, Yassine Wang, Shu-Lin El Jourmi, Mohammed Ouahmane, Hassan Chouaib Doukkali Univ Natl Sch Appl Sci ENSAJ Networks & Comp Sci LTI Lab Dept Telecommun El Jadida 24002 Morocco Hunan Univ Sch Comp Sci & Elect Engn Changsha 41182 Peoples R China

ISBN: (纸本)9781728137834

Cancer may be a title given to all kind of infections caused by hereditary changes, such as substantial changes in DNA. Cancers are caused by mutations-which occur during cell division- that may possibly be inherited, provoked by environmental determinants, or result from some random DNA replication errors which simply maybe considered as bad luck. Recently in bioinformatics, one of the most heated topics is to search about the separation between driver mutations that lead to cancer and passenger mutations, which are well noted to be neutral and do not have any part in this disease development. Indeed, to solve this problem, several approaches and techniques were used in the literature. The main aim of this work is to show a new computation tool called "The Maximum Weight Submatrix Solver (MWS Solver)" using Python language. In fact, we programmed three metaheuristics called: Genetic Algorithm, evolutionary programming and Random Optimization Algorithm, as an approximate approach in order to maximize the weight and sample all the possible driver pathways that can be found. A comparison of these approximate methods with other previous exact and random methods has also been taken into consideration. The experimental tests demonstrate that the proposed algorithms implemented via MWS Solver may become a useful open source and free complementary instrument for finding cancer pathways.

关键词： Maximum Weight Submatrix Problem Driver Pathways Driver Mutation Genetic Algorithm Random Algorithm evolutionary programming

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Data Flows Mapping in Fog Computing Infrastructures Using evolutionary Inspired Heuristic 1

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9th International Conference on Cloud Computing and Services Science (CLOSER)

作者： Canali, Claudia Lancellotti, Riccardo Univ Modena & Reggio Emilia Dept Engn Enzo Ferrari Modena Italy

ISBN: (数字)9783030494322

ISBN: (纸本)9783030494315;9783030494322

The need for scalable and low-latency architectures that can process large amount of data from geographically distributed sensors and smart devices is a main driver for the popularity of the fog computing paradigm. A typical scenario to explain the fog success is a smart city where monitoring applications collect and process a huge amount of data from a plethora of sensing devices located in streets and buildings. The classical cloud paradigm may provide poor scalability as the amount of data transferred risks the congestion on the data center links, while the high latency, due to the distance of the data center from the sensors, may create problems to latency critical applications (such as the support for autonomous driving). A fog node can act as an intermediary in the sensor-to-cloud communications where pre-processing may be used to reduce the amount of data transferred to the cloud data center and to perform latency-sensitive operations. In this book chapter we address the problem of mapping sensors over the fog nodes with a twofold contribution. First, we introduce a formal model for the mapping model that aims to minimize response time considering both network latency and processing time. Second, we present an evolutionary-inspired heuristic (using Genetic Algorithms) for a fast and accurate resolution of this problem. A thorough experimental evaluation, based on a realistic scenario, provides an insight on the nature of the problem, confirms the viability of the GAs to solve the problem, and evaluates the sensitivity of such heuristic with respect to its main parameters.

关键词： Fog computing Optimization model evolutionary programming Genetic algorithms Smart cities

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A new binary-coded approach to the unit commitment problem using Grey wolf optimization 28

A new binary-coded approach to the unit commitment problem u...

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28th Iranian Conference on Electrical Engineering (ICEE)

作者： Majidi, H. Emadaleslami, M. Haghifam, M. R. Tarbiat Modares Univ Fac Elect & Comp Engn Tehran Iran

ISBN: (纸本)9781728172965

One of the most difficult problems in the management of economically power systems is the unit commitment problem. The mathematical complexity of the problem increases with the increase in unit numbers. Given the high dimensions of this problem, meta-heuristic methods can act as one of the best ways of solving this problem. one of the challenges that meta-heuristic strategies faces is the low number of feasible answers compared to non-feasible ones while trying to satisfy the unit commitment constraints which can cause problems in the convergence of these methods. A new approach based on binary encoding in the population was proposed in this paper, using the Gray Wolf Optimization Algorithm (GWO), to solve the complexity of the unit commitment problem. The GWO is then applied to 10 thermal test units. Comprehensive numerical tests are carried out to check the GWO's efficacy using the approach proposed. The simulation results are presented and compared to numerous current heuristic methods that show the GWO's superior performance using the method proposed to solve the UC problem.

关键词： unit commitment (UC) grey wolf optimization (GWO) evolutionary programming constrained optimization binary-coded

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A hybrid evolutionary programming approach for optimal worst case tolerance design of magnetic devices

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APPLIED SOFT COMPUTING 2012年第8期12卷 2425-2434页

作者： Salcedo-Sanz, Sancho Perez-Bellido, Angel M. Ortiz-Garcia, Emilio G. Portilla-Figueras, Jose A. Jimenez-Fernandez, Silvia Univ Alcala de Henares Dept Signal Theory & Commun Alcala De Henares 28871 Spain

This paper presents a hybrid evolutionary programming approach to solve the worst case tolerance design problem (WCTDP) in magnetic devices. The hybrid algorithm is formed by a basic evolutionary programming approach, mixed with a gradient-guided local search. Two different local searches procedures are tested in the paper, both specially designed to be effective in the WCTDP. Simulations on an example in the design of a magnetic circuit and comparison with several existing bio-inspired heuristics are carried out, and have shown the goodness of our algorithm. (C) 2012 Elsevier B. V. All rights reserved.

关键词： evolutionary programming Hybrid algorithms Tolerance analysis Tolerance design

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Structural design of four-component optically compensated zoom lenses: Use of evolutionary programming

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OPTIK 2012年第17期123卷 1534-1541页

作者： Pal, S. Hazra, L. N. Univ Calcutta Dept Appl Opt & Photon Kolkata India

A new approach for 'ab initio' synthesis of thin lens structure of optically compensated zoom lenses is reported. This is accomplished by an implementation of evolutionary programming that explores the available configuration space formed by powers of individual components and inter-component separations to obtain globally or quasiglobally optimum solutions for the problem. Normalization of the variables is carried out to get an insight on the optimum structures. The method has been successfully used to get thin lens structures of optically compensated zoom lens systems by suitable formulation of merit function of optimization. Investigations have been carried out on four-component zoom lens structures. Illustrative numerical results are presented. (C) 2011 Elsevier GmbH. All rights reserved.

关键词： Optically compensated zoom lens Zoom lens evolutionary programming Genetic algorithm Structural design Thin lens design

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An evolutionary programming based asymmetric weighted least squares support vector machine ensemble learning methodology for software repository mining

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INFORMATION SCIENCES 2012年 191卷 31-46页

作者： Yu, Lean Chinese Acad Sci Inst Syst Sci Acad Math & Syst Sci MADIS Beijing 100190 Peoples R China Univ Elect Sci & Technol China Sch Management & Econ Chengdu 610054 Peoples R China Hangzhou Normal Univ Hangzhou Key Lab E Business & Informat Secur Hangzhou 310036 Zhejiang Peoples R China

In this paper, a novel evolutionary programming (EP) based asymmetric weighted least squares support vector machine (LSSVM) ensemble learning methodology is proposed for software repository mining. In this methodology, an asymmetric weighted LSSVM model is first proposed. Then the process of building the EP-based asymmetric weighted LSSVM ensemble learning methodology is described in detail. Two publicly available software defect datasets are finally used for illustration and verification of the effectiveness of the proposed EP-based asymmetric weighted LSSVM ensemble learning methodology. Experimental results reveal that the proposed EP-based asymmetric weighted LSSVM ensemble learning methodology can produce promising classification accuracy in software repository mining, relative to other classification methods listed in this study. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Asymmetric weighted least squares support vector machine evolutionary programming Ensemble learning algorithm Software repository mining

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Diversity Guided evolutionary programming: A novel approach for continuous optimization

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APPLIED SOFT COMPUTING 2012年第6期12卷 1693-1707页

作者： Alam, Mohammad Shafiul Islam, Md. Monirul Yao, Xin Murase, Kazuyuki Bangladesh Univ Engn & Technol Dept Comp Sci & Engn Dhaka 1000 Bangladesh Univ Sci & Technol China Dept Comp Sci Nat Inspired Computat & Applicat Lab Hefei 230027 Anhui Peoples R China Univ Birmingham Sch Comp Sci CERCIA Birmingham B15 2TT W Midlands England Univ Fukui Dept Human & Artificial Intelligence Syst Fukui 9108507 Japan

Avoiding premature convergence to local optima and rapid convergence towards global optima has been the major concern with evolutionary systems research. In order to avoid premature convergence, sufficient amount of genetic diversity within the evolving population is considered necessary. Several studies have focused to devise techniques to control and preserve population diversity throughout the evolution. Since mutation is the major operator in many evolutionary systems, such as evolutionary programming and evolutionary strategies, a significant amount of research has also been done for the elegant control and adaptation of the mutation step size that is proper for traversing across the locally optimum points and reach for the global optima. This paper introduces Diversity Guided evolutionary programming, a novel approach to combine the best of both these research directions. This scheme incorporates diversity guided mutation, an innovative mutation scheme that guides the mutation step size using the population diversity information. It also takes some extra diversity preservative measures to maintain adequate amount of population diversity in order to assist the proposed mutation scheme. An extensive simulation has been done on a wide range of benchmark numeric optimization problems and the results have been compared with a number of recent evolutionary systems. Experimental results show that the performance of the proposed system is often better than most other algorithms in comparison on most of the problems. (C) 2012 Elsevier B. V. All rights reserved.

关键词： evolutionary programming Population diversity Exploitation and exploration Continuous optimization

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