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genetic optimization via Diverse Crossover Intelligence

Journal of Applied Mathematics and Physics 2024年第8期12卷 2885-2903页

作者： David Webb Eric Sandgren AdvanSix Inc. Parsippany New Jersey USA Systems Engineering Department Donaghey College of Engineering & Information Technology University of Arkansas at Little Rock Little Rock Arkansas USA

An intelligent crossover methodology within the genetic algorithm (GA) is explored within both mathematical and finite element arenas improving both design and solution convergence time. This improved intelligent crossover outperforms the traditional genetic algorithm combined with a rule-based approach utilizing domain specific knowledge developed by Webb, et al. [1]. The encoding of the improved crossover consists of two chromosome strings within the genetic algorithm where the first string represents the design or solution string, and the second string represents chromosome crossover string intelligence. This improved crossover methodology saves the best population members or designs evaluated from each generation and applies crossover chromosome intelligence to the best saved population members paired with globally selected parents. Enhanced features of this crossover methodology employ the random selection of the best designs from the prior generation as a potential parent coupled with alternating intelligence pairing methods. In addition to this approach, two globally selected parents possess the ability to mate utilizing crossover chromosome string intelligence maintaining the integrity of a global GA search. Overall, the final population following crossover employs both global and best generation design chromosome strings to maximize creativity while enhancing the solution search. This is a modification to a conventional GA that can be translated into GA encoding. This technique is explored initially through a Base 10 mathematical application followed by the examination of plate structural optimization considering stress and displacement constraints. Results from crossover intelligence are compared with the conventional genetic algorithm and from Webb, et al. [1] which illustrates the outcome of a two phase genetic optimization algorithm.

关键词： Crossover Topological Design Structural optimization genetic optimization Variable Material Design

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genetic optimization of reliability design of machine element

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中国有色金属学会会刊：英文版 2001年第2期11卷 315-318页

作者：郭观七喻寿益中南大学机电工程学院湖南长沙 410083

Canonical genetic algorithms have the defects of prematurity and stagnation when applied in optimization problems. The causes resulting in such phenomena were analyzed and a class of improved genetic algorithm with niche implemented by crossover of similar individuals and ( μ+λ ) selection was proposed. According to the reliability design theory of machine components, the genetic optimization model of jack clutch was obtained. An optimization instance and some results calculated by improved genetic algorithm were presented. The results of emulations and application show that the improved genetic algorithm with the niche technique can achieve the reliable global convergence and stable convergent velocity almost without any additional calculation expense. [

关键词： genetic optimization niche mechanism design reliability design

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A Novel Approach of Hybrid genetic optimization with Elite Ant Colony:Application in the Modulation Recognition

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Wuhan University Journal of Natural Sciences 2011年第1期16卷 43-48页

作者： LIU Shu WANG Hongyuan LANG Liang Department of Electronics and Information Engineering Huazhong University of Science and Technology Wuhan 430074Hubei China

A novel approach （HGO-EAC） for hybrid genetic op-timization （GO） with elite ant colony （EAC） is proposed for the automatic modulation recognition of communication signals,through which we improve the basic ant colony algorithms by referencing elite strategy and present a new fusion strategy for genetic optimization and elite ant colony. This approach is used to train the neural networks as the classifier for modulation. Simula-tion results indicate good performance on an additive white Gaus-sian noise （AWGN） channel,with recognition rate reaching to 70% especially for CW even at signal-to-noise ratios as low as 5 dB. This approach can achieve a high recognition rate for the typical modulations such as CW,4ASK,4FSK,BPSK,and QAM16. Test result shows that it has better performance than BP algorithm and basic ant colony algorithms by achieving faster training and stronger robustness.

关键词： elite ant colony fusion strategy genetic optimization neural networks classification performance modulation recognition

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Improving nonlinear performance of the HEPS baseline design with a genetic algorithm

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Chinese Physics C 2016年第7期40卷 153-157页

作者：焦毅 Key Laboratory of Particle Acceleration Physics and Technology Institute of High Energy PhysicsChinese Academy of Sciences

A baseline design for the High Energy Photon Source has been proposed, with a natural emittance of 60 *** within a circumference of about 1.3 kilometers. Nevertheless, the nonlinear performance of the design needs further improvements to increase both the dynamic aperture and the momentum acceptance. In this study, genetic optimization of the linear optics is performed, so as to find all the possible solutions with weaker sextupoles and hence weaker nonlinearities, while keeping the emittance at the same level as the baseline design. The solutions obtained enable us to explore the dependence of nonlinear dynamics on the working point. The result indicates that with the same layout, it is feasible to obtain much better nonlinear performance with a delicate tuning of the magnetic field strengths and a wise choice of the working point.

关键词： High Energy Photon Source nonlinear performance working point genetic optimization

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An Improved Quantum Evolution Algorithm and Performance Analysis

An Improved Quantum Evolution Algorithm and Performance Anal...

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2012年计算机应用与系统建模国际会议

作者： Zhang Yi Yang Xiuxia Zhou Shaolei Department of Control Engineering Naval Aeronautical and Astronautical University

To tackle the shortcoming of deficient using of feedback information in quantum-inspired evolutionary computing and catastrophe such as quantum mutation, the improved quantum-inspired evolutionary algorithm (IQEA) is proposed. The crossover and mutation operators of genetic algorithm are introduced to the quantum evolution algorithm, at the same time, the better individuals information is applied to the generation of the population applying the probability evaluation model of the estimation of distribution algorithm. Theory analyze and test function simulation experiments show that the improved quantum genetic algorithm is characterized by rapid convergence , excellent robustness and so on.

关键词： Multiple quantum computation quantum-inspired evolutionary algorithm genetic optimization estimation of distribution

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