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检索条件"主题词=Evolutionary algorithm"

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Preserving Coupled Nodes in Population-Based CNP Solvers by Clustering-Elitism Search

Preserving Coupled Nodes in Population-Based CNP Solvers by ...

引用

IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Feng, Mingyang Zhao, Qi Zhang, Liang He, Shan Shi, Yuhui Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England

ISBN: (纸本)9781728190488

Critical node problems (CNP) are important in many fields such as network immunization and viral marketing. Although population-based metaheuristics have been successful on CNP, they fail to efficiently identify and preserve the coupled critical nodes. To narrow this gap, this paper proposes a new search operator called clustering-elitism search for population-based CNP solvers. The proposed operator mainly consists of two parts: clustering and generating. The clustering part divides populations into several clusters to differentiate solutions with various structures, while the generating parts produces new solutions by investigating the consensus and difference between the elite solutions with different structures. Experimental results on synthesis and real networks demonstrate the better performance of the proposed operator with respect to existing solvers.

关键词： Critical node problem evolutionary algorithm population-based search

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Multi-objective optimization based on self-adaptive differential evolution algorithm

Multi-objective optimization based on self-adaptive differen...

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IEEE Congress on evolutionary Computation

作者： Huang, V. L. Qin, A. K. Suganthan, P. N. Tasgetiren, M. F. Nanyang Technol Univ Sch Elect & Elect Engn 50 Nanyang Ave Singapore 639798 Singapore Fatih Univ Dept Management TR-34500 Istanbul Turkey

ISBN: (纸本)9781424413393

In this paper, our recently developed Self-adaptive Differential Evolution algorithm (SaDE) is extended to solve numerical optimization problems with multiple conflicting objectives. The performance of the proposed MOSaDE algorithm is evaluated on a suit of 19 benchmark problems provided for the CEC2007 special session (http://***/home/epnsugan/)on Performance Assessment of Multi-Objective Optimization algorithms.

关键词： differential evolution algorithms self-adaptive Optimization algorithms Benchmark testing evolutionary algorithm Performance assessment

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Learning Individual Mating Preferences 11

Learning Individual Mating Preferences

引用

13th Annual Genetic and evolutionary Computation Conference (GECCO)

作者： Guntly, Lisa M. Tauritz, Daniel R. Missouri Univ Sci & Technol Dept Comp Sci Nat Computat Lab Rolla MO USA

ISBN: (纸本)9781450305570

Mate selection is a key step in evolutionary algorithms which traditionally has been panmictic and based solely on fitness. Various mate selection techniques have been published which show improved performance due to the introduction of mate restrictions or the use of genotypic/phenotypic features. Those techniques typically suffer from two major shortcomings: (1) they are fixed for the entire evolutionary run, which is suboptimal because problem specific mate selection may be expected to outperform general purpose mate selection and because the best mate selection configuration may be dependent on the state of the evolutionary run, and (2) they require problem specific tuning in order to obtain good performance, which often is a time consuming manual process. This paper introduces two versions of Learning Individual Mating Preferences (LIMP), a novel mate selection technique in which characteristics of good mates are learned during the evolutionary process. Centralized LIMP (C-LIMP) learns at the population level, while Decentralized LIMP (D-LIMP) learns at the individual level. Results are presented showing D-LIMP to outperform a traditional genetic algorithm (TGA), the Variable Dissortative Mating Genetic algorithm (VDMGA), and C-LIMP on the DTRAP and MAXSAT benchmark problems, while both LIMP techniques perform comparably to VDMGA on NK Landscapes.

关键词： Reinforcement Learning Mate Selection evolutionary algorithm

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Big-Data-Enabled Modelling and Optimization of Granular Speed-based Vessel Schedule Recovery Problem 5

Big-Data-Enabled Modelling and Optimization of Granular Spee...

引用

IEEE International Conference on Big Data (IEEE Big Data)

作者： Cheraghchi, Fatemeh Abualhaol, Ibrahim Falcon, Rafael Abielmona, Rami Raahemi, Bijan Petriu, Emil Larus Technol Corp Res & Engn Div Ottawa ON Canada Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON Canada

ISBN: (纸本)9781538627150

The Automatic Identification System (AIS) is a vessel tracking system that automatically provides updates on a vessel's movement and other relevant voyage data to vessel traffic management centres and operators. Aside from assisting in real-time tracking and monitoring marine traffic, this system is used in the analysis of historical navigation patterns. In this work, we mined and aggregated vessel speeds from AIS messages within geohashed regions at different precision levels. This granulated, real-world information was brought into the formulation of a Speed-based Vessel Schedule Recovery Problem (S-VSRP). The goal is to mitigate disruptions in vessel schedule by adjusting the speeds while also conforming to the historical navigation patterns reflected in the AIS data. We introduce a new model for vessel schedule speed recovery problem by formulating it as a multi-objective optimization (MOO) problem called the Big-Data-enabled Granular S-VSRP (G-S-VSRP) and propose meta-heuristic optimization methods to find Pareto-optimal solutions. The three objectives are: (1) minimizing the total delay between origin and destination ports, (2) minimizing total financial loss, and (3) maximizing the average speed compliance with historical speed limits. Three evolutionary multi-objective optimizers (EMOO) were investigated and utilized to approximate the Pareto-optimal solutions providing vessel voyage speeds. The Pareto front gives the ability to inspect the tradeoff among the three conflicting objectives. To the best of our knowledge, this is the first time historical AIS data has been exploited in the published literature to mitigate disruptions in vessel schedules.

关键词： Geohashed speed mining evolutionary algorithm Multi-objective optimization Disruption Management Vessel Schedule Recovery Problem Meta-heuristic Automatic Identification System (AIS)

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Design of IIR Digital Filter using modified chaotic orthogonal imperialist competitive algorithm

Design of IIR Digital Filter using modified chaotic orthogon...

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13th Iranian Conference on Fuzzy Systems (IFSC)

作者： Sharifi, MohammadAli Mojallali, Hamed Islamic Azad Univ Dept Elect Comp & Biomed Engn Qazvin Branch Qazvin Iran Univ Guilan Fac Engn Dept Elect Engn Rasht Iran

ISBN: (纸本)9781479912278;9781479912285

There are two types of digital filters including Infinite Impulse Response (IIR) and Finite Impulse Response (FIR). IIR filters attract more attention as they can decrease the filter order significantly compared to FIR filters. Owing to multi-modal error surface, simple powerful optimization techniques should be utilized in designing IIR digital filters to avoid local minimum. Imperialist competitive algorithm (ICA) is an evolutionary algorithm used in solving optimization problems in recent years. ICA can find global optimum response in a nonlinear searching space. In this paper, performance of chaotic orthogonal imperialist competitive algorithm has been improved through some modifications in it. Then, this modified algorithm has been applied in designing IIR digital filters and their performance has been compared to many evolutionary algorithms.

关键词： IIR Digital Filter Imperialist Competitive algorithm Optimization evolutionary algorithm

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Social Adaptation of Robots for Modulating Self-Organization in Animal Societies 8

Social Adaptation of Robots for Modulating Self-Organization...

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2014 IEEE Eighth International Conference on Self Adaptive and Self Organizing Systems Workshops (SASOW 2014)

作者： Zahadat, Payam Bodi, Michael Salem, Ziad Bonnet, Frank de Oliveira, Marcelo Elias Mondada, Francesco Griparic, Karlo Haus, Tomislav Bogdan, Stjepan Mills, Rob Mariano, Pedro Correia, Luis Kernbach, Olga Kernbach, Serge Schmickl, Thomas Karl Franzens Univ Graz Artificial Life Lab Dept Zool Graz Austria Aleppo Univ Dept Comp Engn Elect & Elect Engn Fac Aleppo Syria Swiss Fed Inst Technol EPFL Robot Syst Lab Lausanne Switzerland Fac Elect Engn & Comp Lab Robot & Intelligent Control Syst Zagreb Croatia Univ Lisbon Fac Ciencias LabMAg Lisbon Portugal Cybertronica Res Res Ctr Adv Robot & Environm Sci Stuttgart Germany

ISBN: (纸本)9781479963782

The goal of the work presented here is to influence the overall behaviour of specific animal societies by integrating computational mechatronic devices (robots) into those societies. To do so, these devices should be accepted by the animals as part of the society and/or as part of the collectively formed environment. For that, we have developed two sets of robotic hardware for integrating into societies of two different animals: zebrafish and young honeybees. We also developed mechanisms to provide feedback from the behaviours of societies for the controllers of the robotic system. Two different computational methods are then used as the controllers of the robots in simulation and successfully adapted by evolutionary algorithms to influence the simulated animals for desired behaviours. Together, these advances in mechatronic hardware, feedback mechanisms, and controller methodology are laying essential foundations to facilitate experiments on modulating self-organised behaviour in mixed animal-robot societies.

关键词： animal society Robot Social Adjustment Societies Controllers Societies Microelectromechanical devices Animals evolutionary algorithm self-organization

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An Experimental Study of Prediction Methods in Robust Optimization Over Time

An Experimental Study of Prediction Methods in Robust Optimi...

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IEEE Congress on evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Fox, Matthew Yang, Shengxiang Caraffini, Fabio De Montfort Univ Sch Comp Sci & Informat Leicester Leics England

ISBN: (纸本)9781728169293

Robust Optimization Over Time (ROOT) is a new method of solving Dynamic Optimization Problems in respect to choosing a robust solution, that would last over a number of environment changes, rather than the approach that chooses the optimal solution at every change. ROOT methods currently show that ROOT can be solved by predicting an individual fitness for a number of future environment changes. In this work, a benchmark problem based on the Modified Moving Peaks Benchmark (MMPB) is proposed that includes an attractor heuristic, that guides optima to a determined location in the environment, resulting in a more predictable optimum. We study a number of time series forecasting methods to test different prediction methods of future fitness values in a ROOT method. Four time series regression techniques are considered as the prediction method: Linear and Quadratic Regression, an Autoregressive model, and Support Vector Regression. We find that there is not much difference in choosing a simple Linear Regression to more advanced prediction methods. We also suggest that current benchmark problems that cannot be predicted will deceive the optimizer and ROOT framework as the peaks may move using a random walk. Results show an improvement in comparison with MMPB used in most ROOT studies.

关键词： Particle Swarm Optimization Computational Modelling Metaheuristic Optimization Benchmark Testing evolutionary algorithm

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High-dimensional Function Optimisation by Reinforcement Learning

High-dimensional Function Optimisation by Reinforcement Lear...

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2010 IEEE World Congress on Computational Intelligence

作者： Wu, Q. H. Liao, H. L. Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ Merseyside England

ISBN: (纸本)9781424481262

This paper presents a new algorithm, Function Optimisation by Reinforcement Learning (FORL), to solve large-scale and complex function optimisation problems. FORL undertakes the dimensional search in sequence, in contrast to evolutionary algorithms (EAs) which are based on the population-based search, and has the ability of memory of history incorporated via estimating and updating of the values of states that have been visited, which is different from EAs that aggregate the individuals of a population towards the best selected in a current population. With its capability of searching in sequence and memory of history, FORL reduces the number of function evaluations (FEs). FORL has been evaluated, in comparison with several EAs, including recently improved evolutionary Programming, Genetic algorithms, Particle Swarm Optimisation and other efficient EAs, on 23 benchmark functions, which represent a range of most challenging optimisation problems. The simulation studies show that FORL, using a smaller number of FEs, offers better performance in finding accurate solutions, in particular for high-dimensional multi-modal function optimisation problems.

关键词： complex function evolutionary algorithm evolutionary computation function evaluation function evaluation large scale function learning (artificial intelligence) multimodal function optimisation problem solving problem solving reinforcement learning

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The Overview of Genetic algorithm with Tree Chromosome Structure to Identify Functions 6

The Overview of Genetic Algorithm with Tree Chromosome Struc...

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6th International Conference on Fuzzy Systems and Data Mining (FSDM)

作者： Matayoshi, Mitsukuni Dept Ind & Informat Sci Ginowan Okinawa Japan

ISBN: (纸本)9781643681351;9781643681344

This paper is a collection of previous studies for function identification by simple genetic algorithm (GA) [1] with tree chromosome structure which has been proposed in [2]-[7], and gives the details more than survey paper. This paper also aims to introduce the studies which were written in Japanese. In this paper, there are five main points. First, a tree chromosome structure, which is the core idea of the studies, is introduced. The tree chromosome structure makes GA succeed in function identification called symbolic regression. Second, the proposed GA with tree chromosome structure succeeded in identifying the target functions from the observed data are shown indeed. The target functions are algebraic functions, primary transcendental functions, time series functions including chaos function, and user-defined one-variable functions. Third, to find function represented with some parentheses, a hierarchical tree chromosome structure is introduced. Forth, some local search methods to aim at the improvement for identification success rate and shortening identification time are introduced. In the end of this paper, the proposed tree and hierarchical tree chromosome structure can be adapted for identifying Boolean functions are laid out.

关键词： Genetic algorithm evolutionary algorithm Function Identification Symbolic Regression Local Search Time Series Function Boolean Function

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Using Shuffled Frog Leaping algorithm to View Selection Problem Subject to Dual Constraints 4

Using Shuffled Frog Leaping Algorithm to View Selection Prob...

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4th Annual International Conference on Network and Information Systems for Computers (ICNISC)

作者： Chen, Yu-Si Sun, Lin-Fu Southwest Jiaotong Univ Coll Informat Sci & Technol Chengdu 610031 Peoples R China

ISBN: (纸本)9781538669563

This paper presents a shuffled frog leaping algorithm (SFLA) based solution to solve the View Selection Problem (VSP) subject to dual constraints, which is often used to accelerate data warehouse queries. Since VSP is both discrete and constrained, a greedy-repaired strategy under dual constraints is proposed to handle unfeasible solutions. This proposed solution also profits from a mutation strategy in order to improve the quality of solutions, particularly to avoid being trapped in local optima. Experimental results show that under different constraints combinations, SFLA is able to find a near-optimal feasible solution, with maximum error less than 1%. Comparisons with GA and PSO show that SFLA has better solution quality and faster convergence rate, and also scales with the problem size.

关键词： materialized view view selection shuffled frog leaping evolutionary algorithm

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