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检索条件"主题词=Population-based algorithms"

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Evolutionary Computation for Modelling and Optimization in Finance 1

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19th International Conference on Computational Statistics (COMPSTAT'2010)

作者： Paterlini, Sandra Univ Modena & Reggio E CEFIN Dept Econ Viale Berengario 51 Modena Italy Univ Modena & Reggio E RECent Modena Italy Ctr Quantitat Risk Anal CEQURA Munich Germany

ISBN: (数字)9783790826043

ISBN: (纸本)9783790826036

In the last decades, there has been a tendency to move away from mathematically tractable, but simplistic models towards more sophisticated and real-world models in finance. However, the consequence of the improved sophistication is that the model specification and analysis is no longer mathematically tractable. Instead solutions need to be numerically approximated. For this task, evolutionary computation heuristics are the appropriate means, because they do not require any rigid mathematical properties of the model. Evolutionary algorithms are search heuristics, usually inspired by Darwinian evolution and Mendelian inheritance, which aim to determine the optimal solution to a given problem by competition and alteration of candidate solutions of a population. In this work, we focus on credit risk modelling and financial portfolio optimization to point out how evolutionary algorithms can easily provide reliable and accurate solutions to challenging financial problems.

关键词： population-based algorithms multi-objective optimization clustering credit risk modelling financial portfolio optimization

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A Smart Scheme for Variable Selection in Partial Opposition-based Differential Evolution

A Smart Scheme for Variable Selection in Partial Opposition-...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Wood, Bradley Patel, Viraj Rahnamayan, Shahryar Ontario Tech Univ Dept Elect Comp & Software Engn Oshawa ON Canada Ontario Tech Univ Nat Inspired Computat Intelligence NICI Lab Dept Elect Comp & Software Engn Oshawa ON Canada

ISBN: (纸本)9781728125473

The concept of opposition-based learning (OBL) has been applied to a growing number of research works, as such we proposed a new scheme which improves effectiveness of a population-based optimization algorithm, called Differential Evolution. In this paper, we propose a novel partial opposition-based DE scheme. Given that the partial OBL reduces the likelihood of converting good variables to their opposites, the main challenge of this technique is still the selection of variables which should be replaced with their opposites. The proposed scheme addresses this issue by creating a reference solution using averaging on the best candidate solutions. This reference solution plays a crucial role in intelligent selection of variables to compute their corresponding the opposite value based on their distance to the set of best solutions. The proposed partial opposition-based scheme is embedded in Differential Evolution (DE) algorithm and compared with opposition-based DE (ODE) and DE using the set of CEC-2014 benchmark functions on dimensions 30, 50, and 100. Experimental results indicate a promising improvement over DE and ODE.

关键词： Differential Evolution (DE) Collective Intelligence population-based algorithms Opposition-based Computation

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Design of Dual Rotor Axial Flux Permanent Magnet Generators with Ferrite and Rare-Earth Magnets 18

Design of Dual Rotor Axial Flux Permanent Magnet Generators ...

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18th IEEE International Power Electronics and Motion Control Conference (IEEE PEMC)

作者： Asefi, Tohid Faiz, Jawad Khan, M. A. Univ Tehran Coll Engn Sch Elect & Comp Engn Tehran Iran Univ Cape Town Dept Elect Engn Cape Town South Africa

ISBN: (纸本)9781538641989

This paper discusses dual rotor axial flux machines with surface mounted and spoke type ferrite permanent magnets (PMs) with concentrated windings;they are introduced as alternatives to a generator with surface mounted Nd-Fe-B magnets which is analyzed in [10]. The output power, voltage, speed and air gap clearance for all the generators are identical. The machine designs are optimized for minimum mass using a population-based algorithm, assuming the same efficiency as the Nd-Fe-B machine. A finite element analysis (FEA) is applied to predict the performance, electromotive force, developed torque, cogging torque, no load losses, leakage flux and efficiency of both ferrite generators and that of the Nd-Fe-B generator. To minimize cogging torque, different rotor pole topologies and different pole arc to pole pitch ratios are investigated by means of 3D FEA. It was found that the surface mounted Ferrite generator topology is unable to develop the nominal electromagnetic torque, and has higher torque ripple and is heavier than the spoke type machine. Furthermore, it was shown that the spoke type Ferrite PM generator has favorable performance and could be an alternative to rare-earth PM generators, particularly in wind energy applications. Finally, the analytical and numerical results are verified using experimental results.

关键词： Axial flux permanent magnet generator dual rotor ferrite permanent magnet generator finite element analysis wind turbines cogging torque population-based algorithms

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Generalized Net Model of Cuckoo Search Algorithm 8

Generalized Net Model of Cuckoo Search Algorithm

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8th IEEE International Conference on Intelligent Systems (IS)

作者： Roeva, Olympia Atanassova, Vassia Bulgarian Acad Sci Inst Biophys & Biomed Engn 105 Acad G Bonchev Str BU-1113 Sofia Bulgaria

ISBN: (纸本)9781509013531

In this paper the apparatus of generalized nets is applied to describe the Cuckoo search (CS). The CS is a metaheuristic population-based algorithm inspired by the brood parasitism of some cuckoo species laying their eggs in the nests of other bird species. In addition, CS is enhanced by the so-called Levy flights rather than by simple isotropic random walks. Preliminary studies show that CS is very promising and could outperform existing algorithms such as Particle Swarm Optimization, Genetic algorithms, and other algorithms. The proposed herewith model provides the opportunity to describe the logic of CS in the terms of the mathematical modeling paradigm of generalized nets.

关键词： generalized net model metaheuristic population-based algorithms cuckoo search

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Clustering Center-based Differential Evolution

Clustering Center-based Differential Evolution

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IEEE Congress on Evolutionary Computation (CEC)

作者： Khosrowshahli, Rasa Rahnamayan, Shahryar Bidgoli, Azam Asilian Ontario Tech Univ Dept Elect Comp & Software Engn Nat Inspired Computat Intelligence NICI Lab Oshawa ON Canada

ISBN: (数字)9781665467087

ISBN: (纸本)9781665467087

In recent years, center-based sampling has demonstrated impressive results to enhance the efficiency and effectiveness of meta-heuristic algorithms. The strategy of the center-based sampling can be utilized at either or both the operation and/or population level. Despite the overall efficiency of the center-based sampling in population-based algorithms, utilization at operation level requires customizing the strategy for a specific algorithm which degrades the scheme's generalization. In this paper, we have proposed a population-level center-based sampling method which is operation independent and correspondingly can be embedded in any population-based optimization algorithm. In this study, we applied the proposed scheme for Differential Evolution (DE) algorithm to enhance the exploration and exploitation capabilities of the algorithm. We cluster candidate solutions and inject the centroid-based samples into the population to increase the overall quality of the population and thus decrease the risk of premature convergence and stagnation. By a high chance, the center-based samples are effectively generated in the promising regions of the search space. The proposed method has been benchmarked by employing CEC-2017 benchmark test suite on dimensions 30, 50, and 100. The results clearly indicate the superiority of the proposed scheme, and a detailed results analysis is provided.

关键词： Evolutionary algorithms Differential Evolution Center-based Sampling Optimization population-based algorithms Clustering

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A new initialization scheme for evolutionary optimization methods

A new initialization scheme for evolutionary optimization me...

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10th World Multi-Conference on Systemics, Cybernetics and Informatics/12th International Conference on Information Systems Analysis and Synthesis

作者： Rahnamayan, Shahryar Univ Waterloo Fac Engn Pattern Anal & Machine Intelligence Lab Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9789806560697

This paper proposes a new population initialization scheme for evolutionary algorithms in order to accelerate their convergence speed. Differential Evolution (DE), an efficient, simple, and robust optimization method has been used for experimental verification. A test suite with 34 well-known benchmark functions has been utilized to compare the parent algorithm, classical DE, and its modified version, equipped by embedding proposed initialization scheme inside that. Details for the new scheme and empirical results are provided.

关键词： global optimization evolutionary algorithms population initialization differential evolution (DE) population-based algorithms

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Attention-oriented Brain Storm Optimization for Multimodal Optimization Problems

Attention-oriented Brain Storm Optimization for Multimodal O...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Yang, Jian Shi, Yuhui Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China

ISBN: (纸本)9781728183923

population-based methods are often used to solve multimodal optimization problems. By combining niching or clustering strategy, the state-of-the-art approaches generally divide the population into several subpopulations to find multiple solutions for a problem at hand. However, these methods only guided by the fitness value during iterations, which are suffering from determining the number of subpopulations, i.e., the number of niche areas or clusters. To compensate for this drawback, this paper presents an Attention-oriented Brain Storm Optimization (ABSO) method that introduces the attention mechanism into a relatively new swarm intelligence algorithm, i.e., Brain Storm Optimization (BSO). By converting the objective space from the fitness space into "attention" space, the individuals are clustered and updated iteratively according to their salient values. Rather than converge to a single global optimum, the proposed method can guide the search procedure to converge to multiple "salient" solutions. The preliminary results show that the proposed method can locate multiple global and local optimal solutions of several multimodal benchmark functions. The proposed method needs less prior knowledge of the problem and can automatically converge to multiple optimums guided by the attention mechanism, which has excellent potential for further development.

关键词： Multimodal Optimization Brain Storm Optimization Attention Mechanism population-based algorithms

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Lower Bounds for Non-elitist Evolutionary algorithms via Negative Multiplicative Drift 16th

Lower Bounds for Non-elitist Evolutionary Algorithms via Neg...

引用

16th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Doerr, Benjamin Inst Polytech Paris Ecole Polytech CNRS Lab Informat LIX Palaiseau France

ISBN: (纸本)9783030581145;9783030581152

A decent number of lower bounds for non-elitist population-based evolutionary algorithms has been shown by now. Most of them are technically demanding due to the (hard to avoid) use of negative drift theorems - general results which translate an expected progress away from the target into a high hitting time. We propose a simple negative drift theorem for multiplicative drift scenarios and show that it can simplify existing analyses. We discuss in more detail Lehre's (PPSN 2010) negative drift in populations method, one of the most general tools to prove lower bounds on the runtime of non-elitist mutation-based evolutionary algorithms for discrete search spaces. Together with other arguments, we obtain an alternative and simpler proof, which also strengthens and simplifies this method. In particular, now only three of the five technical conditions of the previous result have to be verified. The lower bounds we obtain are explicit instead of only asymptotic. This allows to compute concrete lower bounds for concrete algorithms, but also enables us to show that super-polynomial runtimes appear already when the reproduction rate is only a (1-.(n(-1/2))) factor below the threshold. As one particular result, we apply this method and a novel domination argument to show an exponential lower bound for the runtime of the mutation-only simple GA on OneMax for arbitrary population size.

关键词： Runtime analysis Drift analysis Lower bounds population-based algorithms Theory Discrete optimization

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Hybrid Initialization in the Process of Evolutionary Learning 16th

Hybrid Initialization in the Process of Evolutionary Learnin...

引用

16th International Conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Lapa, Krystian Cpalka, Krzysztof Hayashi, Yoichi Czestochowa Tech Univ Inst Computat Intelligence Czestochowa Poland Meiji Univ Dept Comp Sci Tokyo Japan

ISBN: (纸本)9783319590639;9783319590622

population-based algorithms are an interesting tool for solving optimization problems. Their performance depends not only on their specification but also on methods used for initialization of initial population. In this paper a new hybridization approach of initialization methods is proposed. It is based on classification of initialization methods that allow various combination of the methods from each category. To test the proposed approach typical problems related to population-based algorithms were used.

关键词： population-based algorithms population initialization Initialization methods Hybrid initialization

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On the Benefits of populations for the Exploitation Speed of Standard Steady-State Genetic algorithms

On the Benefits of Populations for the Exploitation Speed of...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Corus, Dogan Oliveto, Pietro S. Univ Sheffield Dept Comp Sci Sheffield S Yorkshire England

ISBN: (纸本)9781450361118

It is generally accepted that populations are useful for the global exploration of multi-modal optimisation problems. Indeed, several theoretical results are available showing such advantages over single-trajectory search heuristics. In this paper we provide evidence that evolving populations via crossover and mutation may also benefit the optimisation time for hillclimbing unimodal functions. In particular, we prove bounds on the expected runtime of the standard (mu+1) GA for OneMax that are lower than its unary black box complexity and decrease in the leading constant with the population size up to mu = o (root log n). Our analysis suggests that the optimal mutation strategy is to flip two bits most of the time. To achieve the results we provide two interesting contributions to the theory of randomised search heuristics: 1) A novel application of drift analysis which compares absorption times of different Markov chains without defining an explicit potential function. 2) The inversion of fundamental matrices to calculate the absorption times of the Markov chains. The latter strategy was previously proposed in the literature but to the best of our knowledge this is the first time is has been used to show non-trivial bounds on expected runtimes.

关键词： Genetic algorithms crossover recombination population-based algorithms theory runtime analysis

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