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

作者： Ahlam Berkani Mostafa Belkasmi ENSIAS Mohamed V University in Rabat Morocco

ISBN: (纸本)9781509062287

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

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

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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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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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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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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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Faster Optimization Through genetic Drift 18th

Faster Optimization Through Genetic Drift

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18th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Florescu, Cella Kaufmann, Marc Lengler, Johannes Schaller, Ulysse Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland

ISBN: (纸本)9783031700705;9783031700712

The compact genetic algorithm (cGA), parameterized by its hypothetical population size K, offers a low-memory alternative to evolving a large offspring population of solutions. It evolves a probability distribution, biasing it towards promising samples. For the classical benchmark ONEMAX, the cGA has two different modes of operation: a conservative one with small step sizes Theta(1/(root n log n)), which is slow but prevents genetic drift, and an aggressive one with large step sizes Theta(1/ log n), in which genetic drift leads to wrong decisions, but those are corrected efficiently. On ONEMAX, an easy hill-climbing problem, both modes lead to optimization times of Theta(n log n) and are thus equally efficient. In this paper we study how both regimes change when we replace ONEMAX by the harder hill-climbing problem DYNAMIC BINVAL. It turns out that the aggressive mode is not affected and still yields quasi-linear runtime O(n polylog n). However, the conservative mode becomes substantially slower, yielding a runtime of Omega(n(2)), since genetic drift can only be avoided with smaller step sizes of O(1/n). We complement our theoretical results with simulations.

关键词： compact genetic algorithm genetic Drift Estimation-of-Distribution algorithm Dynamic Binary Value

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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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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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Evolutionary computation to design additively manufactured optimal heterogeneous lattice structures

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PROGRESS IN ADDITIVE MANUFACTURING 2023年第3期8卷 615-627页

作者： Despres, Nathaniel Cyr, Edward Setoodeh, Peyman Mohammadi, Mohsen Univ New Brunswick Marine Addit Mfg Ctr Excellence MAMCE Fredericton NB E3B 5A3 Canada

For many industries, reducing the weight and increasing the strength of structural parts are of utmost importance, especially in the transportation sector. With the availability and improvements in additive manufacturing, new lightweight designs can be fabricated more easily, lattice structures being the most common. This is the reason that research on lattice structures has increased to reduce weight and increase strength. The research presented in this paper includes the creation of a new lattice structure design and optimization framework that develops and optimizes fully heterogeneous lattice structures using global optimization algorithms. This paper also compares two global optimization algorithms, the genetic algorithm (GA) and the compact genetic algorithm (cGA), to determine which algorithm is better suited for the design and optimization framework. The results of the various tests performed for this comparison show that the cGA can find better-performing structures and use less computation time than the GA. Some of the best structures produced by the cGA, during the tests, have observable patterns that seem to improve their mechanical performance.

关键词： Additive manufacturing Lattice structure design and analysis genetic algorithm compact genetic algorithm

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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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