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Practical performance models of algorithms in evolutionary program induction and other domains

ARTIFICIAL INTELLIGENCE 2010年第15期174卷 1254-1276页

作者： Graff, Mario Poli, Riccardo Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ Essex England

evolutionary computation techniques have seen a considerable popularity as problem solving and optimisation tools in recent years. Theoreticians have developed a variety of both exact and approximate models for evolutionary program induction algorithms. However, these models are often criticised for being only applicable to simplistic problems or algorithms with unrealistic parameters. In this paper, we start rectifying this situation in relation to what matters the most to practitioners and users of program induction systems: performance. That is, we introduce a simple and practical model for the performance of program-induction algorithms. To test our approach, we consider two important classes of problems - symbolic regression and Boolean function induction - and we model different versions of genetic programming, gene expression programming and stochastic iterated hill climbing in program space. We illustrate the generality of our technique by also accurately modelling the performance of a training algorithm for artificial neural networks and two heuristics for the off-line bin packing problem. We show that our models, besides performing accurate predictions, can help in the analysis and comparison of different algorithms and/or algorithms with different parameters setting. We illustrate this via the automatic construction of a taxonomy for the stochastic program-induction algorithms considered in this study. The taxonomy reveals important features of these algorithms from the performance point of view, which are not detected by ordinary experimentation. (c) 2010 Elsevier B.V. All rights reserved.

关键词： evolution algorithms Program induction Performance prediction Algorithm taxonomies Algorithm selection problem

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Decomposing the user-preference in multiobjective optimization

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SOFT COMPUTING 2016年第10期20卷 4005-4021页

作者： Yu, Guo Zheng, Jinhua Shen, Ruimin Li, Miqing Xiangtan Univ Dept Informat Engn Xiangtan 411100 Hunan Peoples R China Brunel Univ Dept Informat Syst & Comp Uxbridge UB8 3PH Middx England

Preference information (such as the reference point) of the decision maker (DM) is often used in multiobjective optimization;however, the location of the specified reference point has a detrimental effect on the performance of multiobjective evolutionary algorithms (MOEAs). Inspired by multiobjective evolutionary algorithm-based decomposition (MOEA/D), this paper proposes an MOEA to decompose the preference information of the reference point specified by the DM into a number of scalar optimization subproblems and deals with them simultaneously (called MOEA/D-PRE). This paper presents an approach of iterative weight to map the desired region of the DM, which makes the algorithm easily obtain the desired region. Experimental results have demonstrated that the proposed algorithm outperforms two popular preference-based approaches, g-dominance and r-dominance, on continuous multiobjective optimization problems (MOPs), especially on many-objective optimization problems. Moreover, this study develops distinct models to satisfy different needs of the DM, thus providing a new way to deal with preference-based multiobjective optimization. Additionally, in terms of the shortcoming of MOEA/D-PRE, an improved MOEA/D-PRE that dynamically adjusts the size of the preferred region is proposed and has better performance on some problems.

关键词： Decomposition evolution algorithms Preference information Decision maker Iterative weight

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BESS: Balanced evolutionary semi-stacking for disease detection using partially labeled imbalanced data

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INFORMATION SCIENCES 2022年 594卷 233-248页

作者： Ning, Zhihan Ye, Ziqing Jiang, Zhixing Zhang, David Chinese Univ Hongkong Shenzhen Shenzhen 518172 Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518172 Peoples R China Shenzhen Res Inst Big Data Shenzhen 518172 Peoples R China

Machine learning offers automatic and objective approaches for disease detection based on biomedical data. However, 1) the percentage of patients in the real world is smaller than that of healthy people;2) the annotations by medical experts are costly. Therefore, medical datasets are often imbalanced and partially labeled. To address this problem, we propose balanced evolutionary semi-stacking (BESS) for disease detection using partially labeled imbalanced (PLI) data. BESS aims to detect illnesses by considering the input of the color, texture, and geometry features extracted from tongue images. Specifically, the proposed method first mitigates the class imbalance problem and leverages the unlabeled data through the so-called balanced evolutionary co-training approach. Then BESS exploits both the data and classifier diversity obtained by balanced evolutionary co-training to improve the performance of the stacking ensemble. We quantitatively evaluate the proposed algorithm based on the PLI tongue image database. BESS achieves the best performance in detecting diabetes mellitus, chronic kidney disease, breast cancer, and chronic gastritis, compared to other state-of-the-art methods. The results of the experiments substantiate the superiority and effectiveness of the proposed algorithm. Codes and datasets have been made publicly available at url: https://***/CUHKSZ-NING/Balanced-evolutionary-Semi-Stacking. (C) 2022 Elsevier Inc. All rights reserved.

关键词： Disease detection Semi-supervised learning Imbalanced data Tongue features evolution algorithms

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Optimal brick layout of masonry walls based on intelligent evolutionary algorithm and building information modeling

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AUTOMATION IN CONSTRUCTION 2021年 129卷

作者： Xu, Chengran Liu, Jiepeng Li, Sheng Wu, Zhou Chen, Y. Frank Chongqing Univ Sch Civil Engn 174 Shazheng St Chongqing 400044 Peoples R China Chongqing Univ Sch Automat 174 Shazheng St Chongqing 400044 Peoples R China Penn State Univ Dept Civil Engn Middletown PA 17057 USA

The Masonry wall (MW) is an important element of buildings. Currently, the detailed brick layout of a nonmodular MW mostly relies on the experience from a bricklayer. The manual building information modeling (BIM) technique can facilitate the modeling of bricks, but is error-prone and time-consuming. This paper presents a BIM-based framework to generate the layouts and brick take-offs automatically. A computation module is developed to find the optimal brick layout using the three-stage optimization. For a MW, the first-stage determines the vertical position of each course and the second and third stages obtain the horizontal positions and sizes of bricks for odd and even courses respectively. The intelligent algorithm neighborhood field optimization is applied to the computation module. The proposed framework is applied in the automated detailing design of MWs with different bonds in two rooms to demonstrate its practicability and efficiency.

关键词： Masonry walls (MWs) Detailing design Brick layout evolution algorithms Building information modeling (BIM)

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Optimization of Holding Force for a Climbing Robot Based on a Differential evolutionary Algorithm

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RECENT ADVANCES IN ELECTRICAL & ELECTRONIC ENGINEERING 2024年第1期17卷 54-59页

作者： Masike, Rujeko Kaur, Karamjit Arora, Rajesh Shridhara, Somalapura Nagappa Amity Univ Haryana Amity Sch Engn & Technol Dept ECE Panchgaon India HITS Univ Hindustan Inst Technol & Sci Deemed Univ Chennai India Amity Univ Haryana Amity Sch Engn & Technol Panchgaon India

Background: The advancements in robotic technology have completely revolutionized day-to-day life. In industrial applications, the implementation of robotics is quite advantageous as it may help in performing dangerous tasks like climbing high walls, working in a high-temperature environment, high radiation exposure conditions ***;This paper presents the design and development of a wall-climbing robot for dam wall inspection using an adaptive aerodynamic adhesion technique. The optimization of a robot design is done using a differential evolutionary ***: In the proposed model, the principle of Bernoulli adhesion is used for designing the suction pad. The optimization of various variables is done using a differential evolutionary algorithm to improve the efficiency and effectiveness of the wall climbing robot ***: The results of the proposed system show that the approach can find an optimal holding force and can be effectively used for applications like dam wall climbing for inspection.

关键词： Bernoulli pad wall-climbing robot evolution algorithms adaptive holding force dynamic variations variable optimization climbing robot

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Testing and Verification of the Deep Neural Networks Against Sparse Pixel Defects 1

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41st International Conference on Computer Safety, Reliability and Security (SAFECOMP)

作者： Szczepankiewicz, Michal Radlak, Krystian Szczepankiewicz, Karolina Popowicz, Adam Zawistowski, Pawel NVIDIA Warsaw Poland Warsaw Univ Technol Warsaw Poland Vay Technol Berlin Germany Silesian Tech Univ Gliwice Poland

ISBN: (数字)9783031148620

ISBN: (纸本)9783031148620;9783031148613

Deep neural networks can produce outstanding results when applied to image recognition tasks but are susceptible to image defects and modifications. Substantial degradation of the image can be detected by automatic or interactive prevention techniques. However, sparse pixel defects may have a significant impact on the dependability of safety-critical systems, especially autonomous driving vehicles. Such perturbations can limit the perception capabilities of the system while remaining undetected by human observer. The effective generation of such cases facilitates the simulation of real-life challenges caused by sparse pixel defects, like occluded or stained objects. This work introduces a novel sparse adversarial attack generation method based on differential evolution strategy. Additionally, we introduce a novel framework for sparse adversarial attack generation, which can be integrated into the safety-critical systems development process. An empirical evaluation demonstrates that the proposed method outperforms and complements state-of-the-art techniques allowing for complete evaluation of an image recognition system.

关键词： Dependability Adversarial attacks Deep learning evolution algorithms Differential evolution

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Hyperspectral imagery vegetation index and temporal analysis for corn yield estimation

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Conference on Monitoring Food Safety, Agriculture and Plant Health

作者： Yao, HB Tian, L Univ Illinois Dept Agr & Biol Engn Urbana IL 61801 USA

ISBN: (纸本)0819451606

Aerial hyperspectral imagery has been used to find the temporal relationship between image and corn yield. A total of five hyperspectral images were taken during the growing season. For each image, the optimal vegetation index was selected among many candidate vegetation indices. At the same time, the optimal band subset was selected to calculate the vegetation index. The optimal band subset has the minimum number of bands and represents the most significant image bands (or wavelength) for yield prediction. The optimization process used the EAVI (evolutionary Algorithm based Vegetation Index generation) algorithm. Results showed that the EAVI algorithm generated the best vegetation index among many comparison indices for yield estimation. For image taken at different date, the algorithm selected a different optimal vegetation index and image bands. The most common sensitive wavelength identified was in the red edge at 700 nm and in the NIR region at 826 nm. This study showed that images taken from the beginning of full canopy coverage to the corn ear formation period provided the best and stable result for corn yield estimation. It is suggested that this period of time during the growing season would have great potential for remote sensing based corn yield prediction.

关键词： remote sensing aerial hyperspectral image yield monitoring temporal resolution vegetation index evolution algorithms

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Using Predictive Model of Mean Monthly Flows for Large Open Reservoirs Hydropower Control 16

Using Predictive Model of Mean Monthly Flows for Large Open ...

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16th International Conference on Water Distribution System Analysis (WDSA)

作者： Mensik, P. Stary, M. Marton, D. Brno Univ Technol Fac Civil Engn Inst Landscape & Water Management Brno 60200 Czech Republic

Conventional hydropower reservoir operations are mostly based on rules or rule curves. The paper describes algorithm which has been created on idea of adaptive control theory. The adaptive control approach uses repeatedly generated medium-term water flow predictions on a several months ahead as inflows into the large open reservoirs. Values of control outflows are searched by evolution algorithm optimization methods. Principle of the predictive model of average monthly flows is introduced in this paper. The algorithm is applied to the operation hydropower control of selected reservoir. (C) 2014 The Authors. Published by Elsevier Ltd.

关键词： Adaptive evolution algorithms hydropower function large open reservoir operative control optimization predictive model

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evolutionary search techniques application in automated lay-planning optimization problem

Evolutionary search techniques application in automated lay-...

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1995 IEEE International Conference on Systems, Man and Cybernetics - Intelligent Systems for the 21st-Century

作者： BOUNSAYTHIP, C MAOUCHE, S NEUS, M UNIV LILLE 1 CTR AUTOMAT LILLEF-59655 VILLENEUVE DASCQFRANCE

ISBN: (纸本)0780325605

The purpose of this paper fits in optimization of apparel shapes layout. The aim is to mark all the shapes to be cut onto the sheet by minimizing unoccupied spaces. As the problem involves many sub-optimal solutions or many local optima, genetic-like algorithm is used to handle the layout process. The application of genetic algorithm (GA) needs parameter encoding. At first, an individual is encoded by combs coordinates of shape description. Strings are of variable length and genetic operators are created to this domain specific encoding. As genetic algorithm used alone is not very efficient, we made attempt to hybridize GA with simulated annealing (SA). Results provided will be compared with GA alone and with our previous results by tree search.

关键词： evolution algorithms HYBRID GENETIC ALGORITHM APPAREL SHAPES LAYOUT

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Hybrid optimization method for strategic control of water withdrawal from water reservoir with using support vector machines 18th

Hybrid optimization method for strategic control of water wi...

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18th International Conference on Water Distribution System Analysis (WDSA)

作者： Mensik, Pavel Marton, Daniel Brno Univ Technol Fac Civil Engn Inst Landscape & Water Management Veveri 95 Brno 60200 Czech Republic

The aim of strategic control is the effort to achieve optimal water resource management (water reservoir). Classic strategic control of water withdrawal from water reservoir are based mainly on the rules and rules curves, which are created by generalization of historical data of water inflows to the reservoirs and water demand. Discharge series are changing in the time due to expected climate change. It is necessary to looking for intelligent water withdrawal control, which will allow to react on these hydrological changes and contribute to the efficient use of accumulated water for ensure water demand. The paper will describe the algorithm based on adaptive control. The normal adaptive control required knowledge of the water flow medium-term prediction into the reservoir. The created algorithm of intelligent water withdrawal control does not require knowledge of hydrological predictions. This control method is based on a suitable combination optimization method with the Support vector machines method. The control algorithm is one of the possible measures to mitigate the negative impacts of droughts and water scarcity. The algorithm of adaptive control is applied to the control of water withdrawal from selected single-reservoir. The results are compared with usual rules for water withdrawal control. (C) 2016 The Authors. Published by Elsevier Ltd.

关键词： Adaptive evolution algorithms Operative Control Reservoir Strategic Control Optimization Support Vector Machines Water Demand Water Resource Water Withdrawal

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