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Robustness analysis of EGFR signaling network with a multi-objective evolutionary algorithm

BIOSYSTEMS 2008年第1期91卷 245-261页

作者： Zou, Xiufen Liu, Minzhong Pan, Zishu Wuhan Univ Sch Life Sci Wuhan 430072 Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China Wuhan Univ Sch Math & Stat Wuhan 430072 Peoples R China

Robustness, the ability to maintain performance in the face of perturbations and uncertainty, is believed to be a necessary property of biological systems. In this paper, we address the issue of robustness in an important signal transduction network-epidermal growth factor receptor (EGFR) network. First, we analyze the robustness in the EGFR signaling network using all rate constants against the Gauss variation which was described as "the reference parameter set" in the previous study [Kholodenko, B.N., Demin, O.V., Mochren, G., Hoek, J.B., 1999. Quantification of short term signaling by the epidermal growth factor receptor. ***. Chem. 274, 30169-30181]. The simulation results show that signal time, signal duration and signal amplitude of the EGRR signaling network are relatively not robust against the simultaneous variation of the reference parameter set. Second, robustness is quantified using some statistical quantities. Finally, a multi-objective evolutionary algorithm (MOEA) is presented to search reaction rate constants which optimize the robustness of network and compared with the NSGA-II, which is a representation of a class of modem multi-objective evolutionary algorithms. Our simulation results demonstrate that signal time, signal duration and signal amplitude of the four key components - the most downstream variable in each of the pathways: R-Sh-G-S, R-PLP, R-G-S and the phosphorylated receptor RP in EGRR signaling network for the optimized parameter sets have better robustness than those for the reference parameter set and the NSGA-II. These results can provide valuable insight into experimental designs and the dynamics of the signal-response relationship between the dimerized and activated EGFR and the activation of downstream proteins. (C) 2007 Elsevier Ireland Ltd. All rights reserved.

关键词： robustness EGFR network signal transduction multi-objective evolutionary algorithm

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An Efficient Design for a multi-objective evolutionary algorithm to Generate DNA Libraries Suitable for Computation

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INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES 2019年第3期11卷 542-558页

作者： Chaves-Gonzalez, Jose M. Martinez-Gil, Jorge Univ Extremadura Dept Comp Sci Escuela Politecn Caceres 10003 Spain Software Competence Ctr Hagenberg Softwarepk 21 A-4232 Hagenberg Im Muhlkreis Austria

The design of reliable DNA libraries that can be used for bio-molecular computing involves several heterogeneous conflicting design criteria that traditional optimization approaches do not fit properly. As it is well known, evolutionary algorithms are very appropriate for solving complex NP-hard optimization problems. However, these approaches take significant computational resources when large instances of complex problems are managed. This is the case for the design of DNA libraries suitable for computation, which involves a set of conflicting design criteria that have to be simultaneously optimized. The problem tackled in this paper involves four objectives and two constraints which are managed at the same time by a tested multi-objective evolutionary algorithm (MOEA) with thousands of individuals in the population. In this context, every computational approach would take several hours of execution time to generate high-quality DNA strands. In this paper, we present an analysis of the parallel MOEA which has been efficiently parallelized with the aim of generating reliable sets of DNA sequences. The results obtained in the study presented here show that the parallel approach is computationally very efficient and that the DNA libraries are highly reliable for computation.

关键词： DNA library multi-objective evolutionary algorithm Parallel metaheuristics DNA sequence design

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An adaptive disturbance multi-objective evolutionary algorithm based on decomposition

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INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL 2022年第4期41卷 306-315页

作者： Shi, Yanfang Shi, Jianguo Hebei Software Inst Baoding 071000 Hebei Peoples R China

In solving multi-objective optimisation problems, the uniformly distributed weight vector of decomposition based multi-objective evolutionary algorithm (MOEA/D) is not completely suitable for the non-uniformly distributed Pareto front (PF). In order to solve the situation above, this paper proposes an adaptive disturbance multi-objective evolutionary algorithm based on decomposition (AD-MOEA/D), which introduces the disturbance individuals and disturbance weight vectors during the evolution. The disturbance individuals maintain the population diversity and improve convergence accuracy. The disturbance weight vectors assist the weight vectors to adjust adaptively and improve the distribution of PF. Besides, both disturbance individuals and disturbance weight vectors are produced according to the actual evolution, which will not participate in evolution when it is not necessary. The experimental results on multi-objective test functions show that the PF optimised by AD-MOEA/D has better convergence and distribution.

关键词： multi-objective evolutionary algorithm disturbance individuals disturbance weight vectors decomposition

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Dual population multi-objective evolutionary algorithm for dynamic co-transformations

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evolutionary INTELLIGENCE 2024年第5-6期17卷 3269-3289页

作者： Yang, Yongkuan Yang, Yanxiang Liao, Binrong Xiamen Univ Technol Sch Elect Engn & Automat Xiamen 361024 Peoples R China Xiamen Key Lab Frontier Elect Power Equipment & In Xiamen 361024 Peoples R China

Solving constrained multi-objective optimization problems is a challenging task and existing algorithms have been struggling to balance constrained convergence and population diversity. To address this problem, this paper proposes a dual-population cooperative algorithm (DPDCA), which maintains two populations and an archive, and can change the search strategy depending on the evolutionary stage. At the early stage of evolution, the two populations are responsible for different tasks, promote rapid convergence of the populations by exchanging solutions, and save promising solutions in the archive. In the later stages of evolution, different local search strategies are emphasized according to the diversity of the main populations and their convergence to obtain a feasible solution set with better convergence. In order to prevent the algorithm from falling into local optimality at a later stage, we introduce the archive to enhance the diversity of the populations, and finally the main populations are explored in depth to obtain the set of solutions located in the PF. To test the superiority of the algorithm, we tested it against five state-of-the-art algorithms on 28 benchmark test problems and 3 real-world problems, proving that the altered algorithm has good competitiveness in dealing with constrained multi-objective optimization problems.

关键词： Dual-population multi-objective evolutionary algorithm Archives Dynamic synergy strategy

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A novel model-based multi-objective evolutionary algorithm

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INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2016年第2期7卷 177-189页

作者： Wang, Maocai Dai, Guangming Peng, Lei Song, Zhiming Mo, Li China Univ Geosci Sch Comp Wuhan 430074 Peoples R China China Univ Geosci Hubei Key Lab Intelligent Geoinformat Proc Wuhan 430074 Peoples R China

In multi-objective evolutionary algorithm (MOEA), modelling method is a crucial part. Moreover, variable linkages enable the modelling process more complex for multi-objective optimisation problems. The Karush-Kulm-Tucker condition shows that the Pareto set of a continuous MOP with m objectives is a piecewise continuous (m-1)-dimensional manifold. How to use this regularity property to model continuous MOP with variable linkages has been the research focus. In this paper, a model-based multi-objective evolutionary algorithm based on regression analysis (MMEA-RA) for continuous multi-objective optimisation problems with variable linkages is put forward. In the algorithm, the optimisation problem is modelled as a promising area in the decision space by a probability distribution, and the centroid of the probability distribution is (m-1)-dimensional piecewise continuous manifold. The least squares algorithm is used to build such a model. Systematic experiments have shown that, compared with two state-of-the-art algorithms, MMEA-RA performs excellent on a set of test instances with variable linkages.

关键词： multi-objective evolutionary algorithm MOEA least squares algorithm model-based algorithm regression analysis

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Super 14 rugby fixture scheduling using a multi-objective evolutionary algorithm

Super 14 rugby fixture scheduling using a multi-objective ev...

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IEEE Symposium on Computational Intelligence in Scheduling

作者： While, Lyndon Barone, Luigi Univ Western Australia Sch Comp Sci & Software Engn Nedlands WA 6009 Australia

ISBN: (纸本)9781424407040

Super 14 Rugby is not only a popular game, but also a hugely profitable business. However, determining a schedule for games in the competition is very difficult, as a number of different, often conflicting, factors must be considered. We propose the use of a multi-objective evolutionary algorithm for deciding such a schedule. We detail the technical details needed to apply a multi-objective evolutionary algorithm to this problem and report on experiments that show the effectiveness of this approach. We compare solutions found by our approach with recent fixtures employed by the organising authority;our results showing significant improvements over the existing solutions.

关键词： fixture scheduling multi-objective evolutionary algorithm super 14

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A multi-objective evolutionary algorithm to Obtain Test Cases With Variable Lengths 11

A Multi-Objective Evolutionary Algorithm to Obtain Test Case...

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13th Annual Genetic and evolutionary Computation Conference (GECCO)

作者： Yano, Thaise Martins, Eliane de Sousa, Fabiano Luis Univ Estadual Campinas Inst Comp Campinas SP Brazil Natl Inst Space Res Space Mech & Control Div Sao Jose Dos Campos SP Brazil

ISBN: (纸本)9781450305570

In this paper a new multi-objective implementation of the generalized extremal optimization (GEO) algorithm, named M-GEO(vsl), is presented. It was developed primarily to be used as a test case generator to find transition paths from extended finite state machines (EFSM), taking into account not only the transition to be covered but also the minimization of the test length. M-GEO(vsl) has the capability to deal with strings whose number of elements vary dynamically, making it possible to generate solutions with different lengths. The steps of the algorithm are described for a general multi-objective problem in which the solution length is an element to be optimized. Experiments were performed to generate test case from EFSM benchmark models using M-GEO(vsl) and the approach was compared with a related work.

关键词： multi-objective evolutionary algorithm model-based testing variable length test case

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A Novel multi-objective evolutionary algorithm Hybrid Simulated Annealing Concept for Recommendation Systems 1

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23rd International Conference on algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Du, Yu Bao, Haijia Li, Ya Southwest Univ Coll Comp & Informat Sci Chongqing 400715 Peoples R China

ISBN: (数字)9789819708598

ISBN: (纸本)9789819708581;9789819708598

Nowadays, recommendation systems have been widely used in information systems and internet applications. In order to solve the problem that most traditional recommendation algorithms mainly focus on accuracy and neglect other requirements such as diversity and novelty, multi-objective evolutionary algorithm has been introduced into recommendation system. But there is still much room for improvement in their performance. In this paper, we design a novel multi-objective evolutionary algorithm for recommendation system with accuracy, diversity, and novelty as its objective functions. In the proposed model, an efficient uniform distribution initialization and a mutation operator that integrates the concept in simulated annealing algorithm has been designed to improve the probability of producing more high-quality offspring. And an adaptive hybrid selection strategy is designed to select more valuable or promising individuals from offspring. Experimental results demonstrate the effectiveness of the proposed algorithm in terms of accuracy, diversity, novelty, and hypervolume compared to some state-of-the-art recommendation algorithms.

关键词： multi-objective evolutionary algorithm Recommendation system Genetic operator

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An Effective multi-objective evolutionary algorithm Based on Decomposition for A Robot Rescue Path Planning Problem 41

An Effective Multi-objective Evolutionary Algorithm Based on...

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第41届中国控制会议

作者： Loulei Dai Hongguo Wang Quan-ke Pan School of Mechanical and Electrical Engineering and Automation Shanghai University Liaocheng Vocational & Technical College

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

The paper addresses a post-disaster robot rescue path planning problem where a number of people are trapped and a robot is used to rescue *** objective functions are to minimize the number of deaths and the number of serious *** first,a path matrix between two trapped people is obtained by using an improved A *** then,an improved multiobjective evolutionary algorithm is proposed to find rescue paths with Pareto optimal or near-optimal *** last,the proposed algorithm is compared with other multi-objective *** experimental results demonstrate the proposed algorithm can effectively plan a shorter path to reduce the number of deaths and serious injuries after disasters.

关键词： Robot Rescue Path planning A~* algorithm multi-objective evolutionary algorithm

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Probability Learning Based multi-objective evolutionary algorithm for Distributed No-Wait Flow-Shop and Vehicle Transportation Integrated Optimization Problem 19th

Probability Learning Based Multi-objective Evolutionary Algo...

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19th International Conference on Advanced Intelligent Computing Technology and Applications (ICIC)

作者： Ding, Ziqi Li, Zuocheng Qian, Bin Hu, Rong Zhang, Changsheng Kunming Univ Sci & Technol Sch Informat Engn & Automat Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Yunnan Key Lab Artificial Intelligence Kunming 650500 Yunnan Peoples R China

ISBN: (纸本)9789819947546;9789819947553

This paper proposes a probability learning based multi-objective evolutionary algorithm (PLMOEA) to solve distributed no-wait flow-shop and vehicle transportation integrated optimization problem (DNFVTIOP). The objectives to be minimized are the total completion time for each factory (TT) and the total transportation cost (TC). Firstly, we propose for the first time the mathematical description of DNFVTIOP that is quite different from previous works. Secondly, a probability learning approach based on two probability matrices are introduced to improve the global search ability of PLMOEA. To be specific, a positioning probability matrix (PM) is used to accumulate the probability of each job occurring at each position in high-quality solutions. And a conjoint structure probability matrix (CM) is used to learn the dependency of two joint jobs in high-quality solutions. Next, the new population is generated through a dedicated cooperation strategy based on the nondominated sorting method. Finally, computational results show that PLMOEA is an effective solution method for DNFVTIOP.

关键词： Probability learning multi-objective evolutionary algorithm distributed no-wait flow-shop vehicle transportation integrated optimization

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