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evolutionary algorithm performance profiles on the adaptive distributed database management problem

BT TECHNOLOGY JOURNAL 2000年第4期18卷 66-77页

作者： Oates, MJ BT Data Collect Dev Grp Ipswich Suffolk England

evolutionary algorithms have been shown to be effective in providing configuration optimisation to dynamic load balancing in distributed database systems and Web sewers. This paper explores the tuning parameter performance profile of such techniques over a variety of problems, including the adaptive distributed database management problem (ADDMP), focusing on a range of interesting and important features. The ability of the evolutionary search process to reliably find good solutions to a dynamic problem in a minimal and consistent run-time is of paramount importance when considering their application to real-time industrial control problems. This paper demonstrates the existence of certain optimal parameter values, particularly for the rate of applied mutation, which are shown to produce consistently good problem solutions in a low number of evaluations with a minimum standard deviation.

关键词： Database systems Load balancing dynamic loading Online resources evolutionary algorithm Self tuning

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evolutionary algorithm for Optimization Regarding the Planning of Topological Facilities in Layout of a Shipyard

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IEEE LATIN AMERICA TRANSACTIONS 2019年第9期17卷 1491-1500页

作者： Azzolini, W. Azzolini, F. Univ Sao Paulo Escola Engn Sao Carlos Sao Carlos SP Brazil UNESP Curso Ciencias Comp Bauru SP Brazil

The purpose of this study is to contribute the approach to the problem of optimization regarding the planning of topological facilities in layout of a shipyard, with the objective of finding a robust solution to the problem by improving the solution space search through refining the genetic operators. For this, the computational results of the evolutionary algorithm proposed by Choi with changes made by the authors, being: 1) the use of the Partially-Matched Crossover (PMX) genetic operator;2) the use of a recursive expression in the topological optimization step in addition to implementing the Biased Random-Key Genetic algorithm (BRKGA) for the purpose of comparing the results. As a plan of the computational experiments two groups of experiments were performed: 1) with the parameters and variables of the work of Choi, in order to validate the efficiency and effectiveness of the AE proposed in this work and;2) with the parameters and variables of the work of Choi with Department 03 fixed in the position of the best solution found in the 1st group of experiments (position 11 of the topological Grid). Each group contains 50 experiments with 100 iterations and variation of the number of individuals from 100 to 80,000 individuals. As a result, a better solution characterized by the reduction of material handling costs, of 11,816 presented by Choi, for 11,489 monetary units of cost, found from the changes made by the authors of the original proposal of the evolutionary algorithm and the use of BRKGA.

关键词： evolutionary algorithm Facilities Planning Minimization of Materials Movement Cost Shipyard Topological Optimization Biased Random-Key Genetic algorithm

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evolutionary algorithm for a genetic robot's personality

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APPLIED SOFT COMPUTING 2011年第2期11卷 2286-2299页

作者： Lee, Kang-Hee Soongsil Univ Dept Global Sch Media Seoul 156743 South Korea

This paper proposes a new concept of the genetic robot characterized by its own robot genome in which each chromosome consists of many genes that contribute to defining the robot's personality. The large number of genes allows for a highly complex system, however it becomes increasingly difficult and time-consuming to ensure reliability, variability and consistency for the robot's personality while manually initializing values for the individual genes. To overcome this difficulty, this paper also proposes an evolutionary algorithm for evolving a genetic robot's personality (EAGRP) in a mobile phone. EAGRP evolves a gene pool that customizes the robot's genome so that it closely matches a simplified set of personality features desired by the user. It acts on a two-dimensional individual, composed of artificial chromosomes, upon which a new masking method, the Eliza-Meme scheme, is used to derive a plausible individual given the restricted preference settings. This paper also proposes a crossover method that enables reproduction for the two-dimensional genome. Finally, an evaluation procedure for individuals is carried out in a virtual environment using tailored perception scenarios. (C) 2010 Elsevier B. V. All rights reserved.

关键词： Genetic robot Artificial creature Artificial chromosome Robot genome Robot personality evolutionary algorithm Mobile phone

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SADPEA: Structure-aware dual probability evolutionary adaptive algorithm for the budgeted influence maximization problem

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INFORMATION SCIENCES 2025年 699卷

作者： Zhu, Enqiang Wang, Haosen Zhang, Yu Ma, Mingyuan Guangzhou Univ Inst Comp Sci & Technol Guangzhou 510006 Peoples R China Peking Univ Sch Comp Sci Beijing 100871 Peoples R China Minist Educ Key Lab High Confidence Software Technol Beijing 100871 Peoples R China

The influence maximization problem aims to identify a set of starting nodes in a social network that can generate the highest possible spread of influence under a given diffusion model. In real-world scenarios, however, the emphasis is usually on budgeted influence maximization, which considers the expenses involved in activating users. The existing techniques struggle to balance node costs and influence and cannot optimally leverage the network's structural information. To address these problems, we propose a structure-aware dual probability evolutionary adaptive (SADPEA) algorithm that considers network structures and node cost. This innovative algorithm integrates advanced graph representation learning techniques, dual probability mutation evolutionary algorithms, and dual-candidate pool adaptive simulated annealing algorithms. By leveraging graph representation learning algorithms, we can expertly map network nodes to low-dimensional vectors, effectively capturing their structural information and relationships. This hybrid approach optimizes the balance between node influence and cost by selectively filtering nodes based on their initial costs. Our method was tested on six real-world social networks of varying scales and types and was compared to seven baseline algorithms. Our approach consistently outperformed the others, demonstrating an impressive balance between efficiency and solution quality.

关键词： evolutionary algorithm Metaheuristic algorithm Budget influence maximization Social network analysis Graph embedding

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evolutionary algorithm for Scheduling Production Jobs and Preventive Maintenance Activities

Evolutionary Algorithm for Scheduling Production Jobs and Pr...

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9th International Conference on Business Process Management (BPM 2011)

作者： Rebai, Maher Kacem, Imed Adjallah, Kondo H. Univ Technol Troyes 12 Rue Marie Curie F-10010 Troyes France Univ Paul Verlaine Metz F-57000 Metz France Ecole Natl dIngEnieurs Metz Metz France

ISBN: (纸本)9783642281143

We propose in this article an evolutionary algorithm for the problem of scheduling N production jobs on M parallel machines. Each machine should be blocked once during the planning horizon for reasons of preventive maintenance. In our study, the maintenance tasks should continuously be performed because the maintenance resources are not sufficient. We aim to find a schedule composed of the production jobs and the maintenance tasks with a minimal preventive maintenance cost and total sum of production job's weighted completion times. Computational experiments are performed on randomly generated instances. The results show that the evolutionary algorithm is able to produce appropriate solutions for the problem.

关键词： Maintenance Scheduling evolutionary algorithm

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evolutionary algorithm Applications in Data Mining 8

Evolutionary Algorithm Applications in Data Mining

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8th IEEE International Conference on Cloud Computing, Data Science and Engineering (Confluence) / Global Technology, Innovation and Enterpreneurship Summit

作者： Sharma, Vasundhara Dubey, Gaurav Amity Univ ASET Noida Uttar Pradesh India

ISBN: (纸本)9781538617199

Data mining is a field with immense scope and helps us extract knowledge from real world, large and complicated data sets. It has various steps including preprocessing, pattern refinement, and post processing. This field has been growing profoundly with various applications in machine learning areas. It has found a great place and application in this field including evolutionary algorithms (EA). The main inspiration behind using EAs is they are greatly robust and have adaptive search methods, which perform in space of candidate solutions. They utilize powerful and efficient search methodologies to carry out a global search in a space solution. The combination of these two DM and EA including the theoretical and applications part made available for various fields for application. The main purpose of this paper is to present maximum number of applications of EA in Data mining field to present a consolidated view to the interested researchers in this aforesaid field.

关键词： evolutionary algorithm data mining classification association rules evolutionary computation

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evolutionary algorithm FOR ENHANCED GAS PATH ANALYSIS IN TURBOFAN ENGINES

EVOLUTIONARY ALGORITHM FOR ENHANCED GAS PATH ANALYSIS IN TUR...

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ASME Turbomachinery Technical Conference and Exposition (Turbo Expo) - Sustainable Energy - Accelerating the Transition by Advancing Turbine Technology

作者： Rootliep, T. O. Visser, W. P. J. Nollet, M. Delft Univ Technol Delft Netherlands KLM Engine Serv Schiphol Netherlands

ISBN: (纸本)9780791884898

Adaptive modelling (AM) based Gas Path Analysis (GPA) is a powerful diagnostic and prognostic technique for turbofan engine maintenance. This involves the assessment of turbofan component condition using thermodynamic models that can iteratively adapt to measurements values in the gas path by changing component condition parameters. The problem with this approach is that newer turbofan engines such as the General Electric GEnx-1B have fewer gas path sensors installed causing the AM equation systems to become underdetermined. To overcome this problem, a novel approach has been developed that combines the AM model with an evolutionary algorithm (EA) optimization scheme and applies it to multiple operating points. Additionally, these newer turbofan engines provide performance data continuously during flight. Information on variable geometry and bleed valve position, active clearance control state and power off-take is included and can be accounted for to further enhance AM model accuracy. A procedure is proposed where the selection of operating points is based on steady-state stability requirements, cycle model operating point uncertainty and parameter outlier filtering. The Gas turbine Simulation Program (GSP) is used as the non-linear GPA modelling environment. A Multiple Operating Point Analysis (MOPA) is chosen to overcome the problem of underdetermination by utilizing multiple data sets at different operating points. The EA finds the best fit of health parameter deviations by minimizing the multi-point objective function using the GSP AM model. A sub-form of the EA class named Differential Evolution (DE) has been chosen as the optimizer. Like all EAs, DE is a parallel direct search method in which a population of parameter vectors evolves following genetic operations towards an optimum output candidate. The resulting hybrid GPA tool has been verified by solving for different simulated deterioration cases of a GSP model. The tool can identify the direction and magn

关键词： gas turbine diagnostics evolutionary algorithm multi-point GPA

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evolutionary algorithm based 9DOF sensor board calibration 12

Evolutionary algorithm based 9DOF sensor board calibration

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12th IEEE International Symposium on Intelligent Systems and Informatics (SISY)

作者： Sarcevic, Peter Pletl, Szilveszter Kincses, Zoltan Univ Szeged Dept Informat Szeged Hungary Subotica Tech Dept Automat Subotica Serbia

ISBN: (纸本)9781479959969

Accelerometers, gyroscopes and magnetometers can be used in a large variety of applications, and their calibration is a very actual problem due to high error rates, especially when errors are integrated in time. Systematic errors from the measurement values can be removed by sensor calibration, thus the applicability of the sensor can be increased. In this paper, a new evolutionary algorithm-based, quick and easy-to-use calibration method is presented. The algorithm has been developed and tested with real measurement data of the above-mentioned sensors. During this work, measurement data have been collected with 9 degree of freedom (9DOF) sensor boards, which are built up of three-axis accelerometer, gyroscope and magnetometer. For accelerometer and magnetometer calibration, bias values, scale factors and non-orthogonality corrections have been calculated, while for the gyroscopes only offsets have been determined.

关键词： sensor calibration accelerometer gyroscope magnetometer evolutionary algorithm

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evolutionary algorithm using converted problems

Evolutionary algorithm using converted problems

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IEEE Region 10 Conference (TENCON)

作者： Li, Yangyang Chen, Zhenghan Wang, Yang Jiao, Licheng Xidian Univ Key Lab Intelligent Percept & Image Understanding Minst Educ China Int Res Ctr Intelligent Percept & Computat Xian 710071 Peoples R China

ISBN: (纸本)9781509025978

Any evolutionary algorithms should conduct biased search in the search space. Most popular strategies for doing so focus on how to select and generate solutions. In this paper, a new strategy is proposed. It completes the task through the transformation of the given problem. A population of converted problem responding various search weights is used, which may be more suitable for evolutionary algorithm to solve. To show the performance of the new strategy, instantiated algorithm is designed. On some trap problems and benchmark problems, the proposed algorithm using converted problems has competitive performance.

关键词： Optimization evolutionary algorithm decision space nonlinear transformation converted problem

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evolutionary algorithm with Phenotype Diversity for Virtual Network Embedding

Evolutionary Algorithm with Phenotype Diversity for Virtual ...

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International Conference on Computing, Networking and Communications (ICNC)

作者： Otoshi, Tatsuya Murata, Masayuki Osaka Univ Grad Sch Econ 1-7 Machikaneyama Cho Toyonaka Osaka 5650043 Japan Osaka Univ Grad Sch Informat Sci & Technol I-5 Yamadaoka Suita Osaka 5650871 Japan

ISBN: (纸本)9781665457194

With the diversification of applications using the Internet, network virtualization technologies that flexibly allocate network resources are attracting attention. In network virtualization, virtual network embedding is important to properly map the requirements of the virtual network to the physical network. However, it takes time to calculate the solution by optimization, and recalculation of the embedding becomes a problem when the environment of the virtual and physical networks changes. Therefore, a method of having multiple solution candidates in advance and switching the solution depending on the situation is considered, but the design and updating of the solution candidates themselves remain an issue. Such a relationship between solution candidates and solution selection is similar to the relationship between genotype and phenotype in biological evolution, and it is a shortcut to get hints from evolution. In biological evolution, the phenotype searches for short-term practical solutions while the genotype continues to search for optimal solutions. By introducing this mechanism into the network, it is possible to select a quasi-optimal solution in a fluctuating environment while continuing the search for a better solution candidate itself. In this paper, we propose a dynamic virtual network embedding method in which the solution candidates themselves can be dynamically updated based on the evolution of genotype and phenotype. In this method, candidate solutions are encoded as genotypes, and phenotypes are decoded by attractor selection using noise-induced fluctuations. Through evaluation, we show that the attractor selection by individuals leads to the discovery of appropriate solutions faster than when using neural networks.

关键词： Novelty Search Attractor Selection Virtual Network Embedding Local Competition evolutionary algorithm

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