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World Summit on genetic and Evolutionary Computation (GEC 09)

作者： Zhang, Qing-bin Wu, Ti-hua Liu, Bo Shijiazhuang Inst Railway Technol Shijiazhuang 050041 Peoples R China

ISBN: (纸本)9781605583266

In order to improve the performance of the compact genetic algorithm (cGA) to solve difficult optimization problems, an improved cGA which named as the weight based compact genetic algorithm (wcGA) is proposed. In the wcGA, S individuals are generated from the probability vector in each generation, when the winner competing with the other S-I individuals to update the probability vector, different weights are multiplied to each solution according to the sequence of the solution ranked in the S-1 individuals. Experimental results on three kinds of Benchmark functions show that the proposed algorithm has higher optimal precision than that of the standard cGA and the cGA simulating higher selection pressures.

关键词： compact genetic algorithm Selection Pressure Weight

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A rigorous analysis of the compact genetic algorithm for linear functions

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Natural Computing 2006年第3期5卷 257-283页

作者： Droste, Stefan LS Informatik 2 Universität Dortmund 44221 Dortmund Germany

Estimation of distribution algorithms (EDAs) solve an optimization problem heuristically by finding a probability distribution focused around its optima. Starting with the uniform distribution, points are sampled with respect to this distribution and the distribution is changed according to the function values of the sampled points. Although there are many successful experiments suggesting the usefulness of EDAs, there are only few rigorous theoretical results apart from convergence results without time bounds. Here we present first rigorous runtime analyses of a simple EDA, the compact genetic algorithm (cGA), for linear pseudo-Boolean functions on n variables. We prove a general lower bound for all functions and a general upper bound for all linear functions. Simple test functions show that not all linear functions are optimized in the same runtime by the cGA. © Springer Science+Business Media, Inc. 2006.

关键词： compact genetic algorithm Runtime Theoretical analysis

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An Optimal Clustering Formation for Wireless Sensor Network Based on compact genetic algorithm 3

An Optimal Clustering Formation for Wireless Sensor Network ...

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International Conference on Robot Vision and Signal Processing

作者： Pan, Tien-Szu Trong-The Nguyen Thi-Kien Dao Chu, Shu-Chuan Natl Kaohsiung Univ Appl Sci Dept Elect Engn Kaohsiung 80778 Taiwan Haiphong Private Univ Fac Informat Technol Haiphong Vietnam Flinders Univ S Australia Sch Comp Sci Engn & Math Adelaide SA Australia

ISBN: (纸本)9781467396462

Challenging task in many practical optimization applications arises from the restricted available hardware devices due to cost or space such as the sensor nodes in wireless sensor networks. This paper proposes a novel hierarchical clustering approach for wireless sensor networks to maintain energy depletion of the network in minimum using compact genetic algorithm. In this proposed method, new candidate solutions can be generated by learning explicit probabilistic models of promising solutions found so far and sampling the built models. The actual population is represented as a probability distribution over the set of solutions and is operationally equivalent to the order-one behavior of the GA. Simulation results compared with the original and the other methods in the literature as LEACH, LEACH-C, and HEED show that the proposed method provides the effective way of using a limited memory and the better performance in terms of the average residual energy, number of nodes alive, and number of received items to save the energy of nodes.

关键词： compact genetic algorithm Wireless sensor network Optimal clustering formation

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Novel compact genetic algorithm for WTA problem

Novel Compact Genetic Algorithm for WTA problem

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International Conference on Measurement, Instrumentation and Automation (ICMIA 2012)

作者： Liu, Zhen Hu, Yunan Naval Aeronaut & Astronaut Univ Dept Control Engn Yantai 264001 Shandong Peoples R China

ISBN: (纸本)9783037855454

The paper proposed a novel compact genetic algorithm which is named as pseudo-parallel compact genetic algorithm. There are two populations in the process of evolution, and the two subpopulation can exchange information between each other. The experimental results show that the novel algorithm performs better than simple genetic algorithm. Then it is used to solve weapon target allocation (WTA) problem, and the simulation result shows that it is more efficient comparing with other methods. Because the compact genetic algorithm is easy to operate and take up less memory, so the algorithm exhibit a better quality of solution and the required less time than before.

关键词： compact genetic algorithm WTA problem Parallel

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Optimizing Ontology Alignment by using compact genetic algorithm 11

Optimizing Ontology Alignment by using Compact Genetic Algor...

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11th International Conference on Computational Intelligence and Security (CIS)

作者： Xue, Xingsi Liu, Jianhua Tsai, Pei-Wei Zhan, Xianyin Ren, Aihong Fujian Univ Technol Sch Informat Sci & Engn Fuzhou Peoples R China Baoji Univ Arts & Sci Dept Math Baoji Peoples R China

ISBN: (纸本)9781467386609

In this paper, we propose a novel approach based on compact genetic algorithm (CGA) to address the problem of optimizing the aggregation of three different basic similarity measures (Syntactic Measure, Linguistic Measure and Taxonomy-based Measure), and get a single similarity metric in the process of ontology matching. Comparing with conventional genetic algorithm (GA), the proposed method is able to dramatically reduce the time and memory consumption while at the same time ensures the correctness and completeness of the alignments. Experiment results show that the proposed approach is effective.

关键词： ontology matching compact genetic algorithm similarity measure

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Not all linear functions are equally difficult for the compact genetic algorithm 05

Not all linear functions are equally difficult for the compa...

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genetic and Evolutionary Computation Conference

作者： Droste, Stefan Univ Dortmund LS Informat 2 D-44221 Dortmund Germany

ISBN: (纸本)1595930108

Estimation of distribution algorithms (EDAs) try to solve an optimization problem by finding a probability distribution focussed around its optima. For this purpose they conduct a sampling-evaluation-adjustment cycle, where search points are sampled with respect to a probability distribution, which is adjusted according to the evaluation of the sampled points. Although there are many successful experiments suggesting the usefulness of EDAs, there are only few rigorous theoretical results apart from convergence results without time bounds. Here we present first rigorous runtime analyses of a simple EDA, the compact genetic algorithm, for linear pseudo-boolean functions on n variables. We prove a number of results showing that not all linear functions have the same asymptotical runtime.

关键词： theoretical analysis runtime compact genetic algorithm

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Improved compact genetic algorithm for Thinned Array Design

Improved Compact Genetic Algorithm for Thinned Array Design

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7th European Conference on Antennas and Propagation (EuCAP)

作者： Bui Van Ha Zich, R. E. Mussetta, M. Pirinoli, Paola Politecn Milan Dept Energy I-20133 Milan Italy Politecn Torino Dept Elect & Telecommun Turin Italy

ISBN: (纸本)9788890701832;9781467321877

A new strategy based on compact genetic algorithm (cGA) for the synthesis of linear thinned arrays is here proposed. In order to exploit all available knowledge from Almost Different Set (ADS) for thinned array to obtain very low peak sidelobe level (PSL) suitable probability vectors to represent the solutions have been introduced. As a proof of concept, several thinned arrays have been synthesized and the obtained results are here discussed.

关键词： Linear thinned array compact genetic algorithm

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Semi-Automatic Ontology Matching Based on Interactive compact genetic algorithm

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2022年第5期36卷

作者： Xue, Xingsi Yang, Chaofan Mao, Guojun Zhu, Hai Fujian Univ Technol Intelligent Informat Proc Res Ctr Fuzhou 350118 Fujian Peoples R China Fujian Univ Technol Sch Comp Sci & Math Fuzhou 350118 Fujian Peoples R China Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Fuzhou 350118 Fujian Peoples R China Zhoukou Normal Univ Sch Network Engn Zhoukou 466001 Henan Peoples R China

Ontology matching is able to identify the entity correspondences between two heterogeneous ontologies, which is an effective method to solve the data heterogeneous problem on the Semantic Web. Traditional fully-automatic ontology matching techniques suffer from the limitation of similarity measure, whose alignment's quality cannot be ensured. To overcome this drawback, in this work, an Interactive compact genetic algorithm (ICGA)-based ontology matching technique is proposed, which utilizes both the compact encoding mechanism and expert interacting mechanism to improve the algorithm's performance and the alignment's quality. In addition, an optimization model is established to formally define the ontology entity matching problem, and an efficient interacting strategy is proposed, which is able to reduce the expert's workload and maximize his working value. The experiment uses Ontology Alignment Evaluation Initiative (OAEI)'s benchmark to test our proposal's performance. The experimental results show that our approach is able to make use of the expert knowledge to improve the alignment's quality, and it also outperforms OAEI's participants.

关键词： Ontology matching interaction compact genetic algorithm

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Application of a compact genetic algorithm to Pipe Network Optimization Problems

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SCIENTIA IRANICA TRANSACTION A-CIVIL ENGINEERING 2009年第3期16卷 264-271页

作者： Afshar, M. H. Iran Univ Sci & Technol Dept Civil Engn Tehran Iran

This paper presents the application of a compact genetic algorithm (cGA) to pipe network optimization problems. A compact genetic algorithm is proposed to reduce the storage and computational requirements of population-based genetic algorithms. A compact CA acts like a standard GA, with a binary chromosome and uniform crossover, but does not use a population. Instead, the cGA represents a virtual population for a binary CA by a vector of probabilities representing the chance that the optimal solution has a one at each bit position. The application of the cGA to pipe network optimization problems is considered in this paper and the results are presented for two benchmark examples and compared with existing solutions in the literature. The results show the ability of the cGA to locate the optimal solution of problems, considered with a computational effort, comparable to improved population-based GAs and with much fewer storage requirements.

关键词： Pipe networks Optimal design compact genetic algorithm

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A novel compact genetic algorithm using offspring survival evolutionary strategy

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ARTIFICIAL LIFE AND ROBOTICS 2009年第4期14卷 489-493页

作者： Seok, Joon-Hong Lee, Ju-Jang Korea Adv Inst Sci & Technol Elect Engn & Comp Sci 373-1 Guseong Dong Daejeon 305701 South Korea

This article describes a compact genetic algorithm (cGA) with an offspring survival evolutionary strategy. The cGA requires less memory than the population-based GA since the whole population is not necessary. The cGA can easily be implemented because it has no complex genetic operator. However, the cGA requires a large amount of fitness evaluation to provide acceptable solutions in problems involving higher-order building blocks (BBs). In order to reduce the number of fitness evaluations, a higher selection pressure is applied to the cGA. Generally, elitism is used to increase the selection pressure. However, elitism may lead to premature convergence as the order of BBs becomes higher. In this article, we propose a balanced cGA using an offspring survival evolutionary strategy. The usefulness of the proposed cGA is verified by comparing it with the original cGA and the elitism-based cGAs using wellknown benchmark functions.

关键词： compact genetic algorithm Offspring survival evolutionary strategy

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