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A dynamic co-evolution compact genetic algorithm for E/T problem

IFAC-PapersOnLine 2015年第28期48卷 1439-1443页

作者： Han, Zhonghua Zhu, Yihang Lin, Shuo Faculty of Information and Control Engineering Shenyang Jianzhu University Department of Digital Factory Shenyang Institute of Automation Key Laboratory of networked control system Shenyang China Faculty of Information and Control Engineering Shenyang Jianzhu University Shenyang China

In this paper, a dynamic co-evolution compact genetic algorithm (DCCGA) is proposed for flexible flow shop scheduling problem (FFSP) to minimize the total earliness and tardiness (E/T) penalties. In this new algorithm, a dynamic co-evolution mechanism containing two probabilistic models and a best individual inheritance strategy are integrated into the compact genetic algorithm (CGA). For improving the stability of the evolutionary trend in the evolution processes, the diversity of evolution trend and the convergence speed. Lastly, the experimental results show that, DCCGA outperforms CGA by 11.74% on the problem we study. © 2015

关键词： genetic algorithms Machine shop practice Co evolution compact genetic algorithm Earliness tardiness Flexible flow shop Probabilistic models

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Medium Step Sizes are Harmful for the compact genetic algorithm 18

Medium Step Sizes are Harmful for the Compact Genetic Algori...

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genetic and Evolutionary Computation Conference (GECCO)

作者： Lengler, Johannes Sudholt, Dirk Witt, Carsten Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland Univ Sheffield Dept Comp Sci Sheffield S Yorkshire England Tech Univ Denmark DTU Compute Lyngby Denmark

ISBN: (纸本)9781450356183

We study the intricate dynamics of the compact genetic algorithm (cGA) on ONEMAX, and how its performance depends on the step size 1/K, that determines how quickly decisions about promising bit values are fixed in the probabilistic model. It is known that cGA and UMDA, a related algorithm, run in expected time O(n log n) when the step size is just small enough (K = Theta(root n log n) to avoid wrong decisions being fixed. UMDA also shows the same performance in a very different regime (equivalent to K = Theta(log n) in the cGA) with much larger steps sizes, but for very different reasons: many wrong decisions are fixed initially, but then reverted efficiently. We show that step sizes in between these two optimal regimes are harmful as they yield larger runtimes: we prove a lower bound Omega(K(1/3)n + n log n) for the cGA on ONEMAX for K = O(root n/log(2) n). For K = Omega(log(3) n) the runtime increases with growing K before dropping again to O(K root n + nlogn) for K = Omega(root n log n). This suggests that the expected runtime for cGA is a bimodal function in K with two very different optimal regions and worse performance in between.

关键词： Estimation-of-distribution algorithms compact genetic algorithm evolutionary algorithms running time analysis theory

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The fuzzy based compact genetic algorithm for online TSP

The fuzzy based compact genetic algorithm for online TSP

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1st WRI Global Congress on Intelligent Systems (GCIS 2009)

作者： Li Shugang Shanghai Jiao Tong Univ Dept Ind Engn & Management Shanghai 200240 Peoples R China

ISBN: (纸本)9780769535715

In this paper, we extend the definition of the common TSP and propose the online TSP, whose purpose is to find the shortest time of visiting all cities while considering the traffic conditions. We also propose a fuzzy based compact genetic algorithm (FCGA) for online TSPs. The basic idea of FCGA is to adapt the size of population simulated of CGA with a fuzzy neural network (FNN) based knowledge machine, which online refines the existing fuzzy rules and infers the fuzzy output directly without the complex fuzzy inference operations.

关键词： online TSP compact genetic algorithm neural network Fuzzy logic

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Simple compact genetic algorithm for XCS

Simple Compact Genetic Algorithm for XCS

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IEEE Congress on Evolutionary Computation

作者： Nakata, Masaya Lanzi, Pier Luca Takadama, Keiki Univ Electrocommun Dept Informat Tokyo Japan Politecn Milan Dipartimento Elettron & Informaz Milan Italy

ISBN: (纸本)9781479904549;9781479904532

This paper proposes a novel rule discovery mechanism for the XCS classifier system, which is an extension of the compact genetic algorithm (cGA) to XCS. Our rule discovery mechanism, like cGA, extracts appropriate attributes of classifier conditions through a probability vector and evolves classifiers using the extracted attributes. Unlike cGA, it newly builds the probability vector at every generations (i.e., it keeps no any probability vectors) not so that it requires XCS to have a lot of probability vectors that represent all available attributes, and mutates classifier conditions based on the extracted attributes as attribute feedback. Experimental results show that XCS with our rule discovery mechanism (or XCScGA) can reach optimal performance with fewer rule evaluations and requires smaller population sizes than XCS. Our conclusion is that the proposed rule discovery mechanism promotes a recombination of building blocks, and that our mutation operator works to repair the classifier conditions towards a compact solutions, hence, XCScGA can generate good offspring which represent maximally general, maximally accurate, and compact solutions.

关键词： Learning Classifier System XCS compact genetic algorithm Generalization

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Updating strategy in compact genetic algorithm using moving average approach

Updating strategy in compact genetic algorithm using moving ...

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IEEE Conference on Cybernetics and Intelligent Systems

作者： Rimcharoen, Sunisa Sutivong, Daricha Chongstitvatana, Prabhas Chulalongkorn Univ Dept Comp Engn Bangkok Thailand

ISBN: (纸本)9781424400225

The compact genetic algorithm (cGA) has a distinct characteristic that it requires almost minimal memory to store candidate solutions. It represents a population structure as a probability distribution over the set of solutions. Although cGA offers many advantages, it has a limitation that hinges on an assumption of the independency between each individual bit. For example, cGA fails to solve a deceptive function or the so called trap function, which is a standard difficult test problem for genetic algorithm. This paper proposes applying a moving average technique to update a probability vector in the compact genetic algorithm. This method requires fewer evaluations and achieves a higher solution quality. The results are compared with the original cGA, sGA, persistent elitist cGA (pe-cGA) and nonpersistent elitist cGA (ne-cGA). The compared results illustrate that the proposed methodology can successfully improve the solution quality by modifying the updating strategy of cGA.

关键词： compact genetic algorithm moving average updating strategy

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A dynamic co-evolution compact genetic algorithm for E/T problem

A dynamic co-evolution compact genetic algorithm for E/T pro...

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17th IFAC Symposium on System Identification (SYSID 2015)

作者： Han, Zhonghua Zhu, Yihang Lin, Shuo Shenyang Jianzhu Univ Fac Informat & Control Engn Shenyang Peoples R China Chinese Acad Sci Shenyang Inst Automat Dept Digital Factory Shenyang Peoples R China Chinese Acad Sci Key Lab Networked Control Syst Shenyang Peoples R China

In this paper. a dynamic co-evolution compact genetic algorithm (DCCGA) is proposed for flexible flow shop scheduling problem (FFSP) to minimize the total earliness and tardiness (E/T) penalties. In this new algorithm, a dynamic co-evolution mechanism containing two probabilistic models and a best individual inheritance strategy are integrated into the compact genetic algorithm (CGA). For improving the stability of the evolutionary trend in the evolution processes, the diversity of evolution trend and the convergence speed. Lastly, the experimental results show that, DCCGA outperforms CGA by 11.74% on the problem we study. (C) 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： probabilistic models dynamic co-evolution compact genetic algorithm flexible flow shop earliness tardiness (E/T)

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A Frequency-Based Updating Strategy in compact genetic algorithm

A Frequency-Based Updating Strategy in Compact Genetic Algor...

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17th International Computer Science and Engineering Conference (ICSEC)

作者： Phiromlap, Srichol Rimcharoen, Sunisa Burapha Univ Fac Informat Chon Buri 20131 Thailand

ISBN: (纸本)9781467353229

The compact genetic algorithm (cGA) is one of evolutionary algorithms. There are proofs in the literature that the cGA mimics the behavior of Simple genetic algorithm (sGA). The cGA has a benefit in requiring almost minimal memory to store candidate solutions. It represents a population as a probability distribution instead of storing whole candidate solutions. Although the cGA has many advantages, it has a limitation on solving some problems such as deceptive problem or so called trap function. Therefore, this paper proposes an adaptation of updating strategy in the compact genetic algorithm to help the algorithm to achieve a higher solution quality with fewer evaluations. We named the proposed technique as the frequency based compact genetic algorithm (fb-cGA). The fb-cGA employs information from the past. We count frequencies and continuity of updating probabilities for both up and down. The frequencies and continuity are used to guide an updating step size. The experiment results show that our proposed method requires fewer evaluations and achieves a higher solution quality than the cGA. It can save the number of fitness evaluations up to ninefold when compared with the cGA using tournament size of 2 on 3x10 trap problem.

关键词： genetic algorithm compact genetic algorithm updating strategy frequency based trap function

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The compact genetic algorithm Struggles on Cliff Functions

The Compact Genetic Algorithm Struggles on Cliff Functions

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genetic and Evolutionary Computation Conference (GECCO)

作者： Neumann, Frank Sudholt, Dirk Witt, Carsten Univ Adelaide Optimisat & Logist Sch Comp Sci Adelaide SA Australia Univ Passau Fac Comp Sci & Math Passau Germany Tech Univ Denmark DTU Compute Lyngby Denmark

ISBN: (纸本)9781450392372

The compact genetic algorithm (cGA) is a non-elitist estimation of distribution algorithm which has shown to be able to deal with difficult multimodal fitness landscapes that are hard to solve by elitist algorithms. In this paper, we investigate the cGA on the Cliff function for which it has been shown recently that non-elitist evolutionary algorithms and artificial immune systems optimize it in expected polynomial time. We point out that the cGA faces major difficulties when solving the Cliff function and investigate its dynamics both experimentally and theoretically around the Cliff. Our experimental results indicate that the cGA requires exponential time for all values of the update strength... We show theoretically that, under sensible assumptions, there is a negative drift when sampling around the location of the cliff. Experiments further suggest that there is a phase transition for.. where the expected optimization time drops from n Theta((n)) to 2 Theta((n)).

关键词： Estimation-of-distribution algorithms compact genetic algorithm evolutionary algorithms running time analysis theory.

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A Reduced Complexity Decoder Using compact genetic algorithm For Linear Block Codes

A Reduced Complexity Decoder Using Compact Genetic Algorithm...

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International Conference on Advanced Communication Systems and Information Security (ACOSIS)

作者： Berkani, Ahlam Belkasmi, Mostafa Mohamed V Univ Rabat ENSIAS SIME Lab Rabat Morocco

ISBN: (纸本)9781509062270

The compact genetic algorithm decoder has been introduced in [9] as an efficient decoding method of linear block codes. It requires less storage memory than genetic algorithms based decoders. One of its major weakness is the big number of necessary iterations to reach convergence in comparison with genetic algorithms (GA) based decoders. We propose, in this work, new ideas allowing us to reduce the number of iterations from about 10(5) to just about 10(3) which reduces the complexity of decoding. This, without decreasing the decoding performance. We introduce a new stopping criterion based on the soft weight of the probability vector p, a new initialization method of p and we tried to combine both methods all together. Both performance study and the calculation of the average number of iterations ensure the effectiveness of the proposed decoder.

关键词： compact genetic algorithm iterations complexity stopping criterion probability vector soft weight initialization

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A Weight Based compact genetic algorithm 09

A Weight Based Compact Genetic Algorithm

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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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