evolutionary computation has experienced a tremendous growth in the last decade in both theoretical analyses and industrial applications. Its scope has evolved beyond its original meaning of ＂biological evolution＂ t...
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evolutionary computation has experienced a tremendous growth in the last decade in both theoretical analyses and industrial applications. Its scope has evolved beyond its original meaning of ＂biological evolution＂ toward a wide variety of nature inspired computational algorithms and techniques, including evolutionary, neural, ecological, social and economical computation, etc, in a unified framework. Many research topics in evolutionary computation nowadays are not necessarily ＂evolutionary＂. This paper provides an overview of some recent advances in evolutionary computation that have been made in CERCIA at the University of Birmingham, UK. It covers a wide range of topics in optimization, learning and design using evolutionary approaches and techniques, and theoretical results in the computational time complexity of evolutionary algorithms. Some issues related to future development of evolutionary computation are also discussed.
From the Publisher: After a decade's development, evolutionary computation (EC) proves to be a powerful tool kit for economic analysis. While the demand for this equipment is increasing, there is no volume exclusi...
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ISBN:
(纸本)3790814768
From the Publisher: After a decade's development, evolutionary computation (EC) proves to be a powerful tool kit for economic analysis. While the demand for this equipment is increasing, there is no volume exclusively written for economists. This volume for the first time helps economists to get a quick grasp on how EC may support their research. A comprehensive coverage of the subject is given, that includes the following three areas: game theory, agent-based economic modelling and financial engineering. Twenty leading scholars from each of these areas contribute a chapter to the volume. The reader will find himself treading the path of the history of this research area, from the fledgling stage to the burgeoning era. The results on games, labour markets, pollution control, institution and productivity, financial markets, trading systems design and derivative pricing, are new and interesting for different target groups. The book also includes informations on web sites, conferences, and computer software.
evolutionary computation(EC)has strengths in terms of computation for gait ***,conventional evolutionary algorithms use typical gait parameters such as step length and swing height,which limit the trajectory deformati...
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evolutionary computation(EC)has strengths in terms of computation for gait ***,conventional evolutionary algorithms use typical gait parameters such as step length and swing height,which limit the trajectory deformation for optimization of the foot ***,the quantitative index of fitness convergence is *** this paper,we perform gait optimization of a quadruped robot using foot placement perturbation based on *** proposed algorithm has an atypical solution search range,which is generated by independent manipulation of each placement that forms the foot trajectory.A convergence index is also introduced to prevent premature cessation of *** conventional algorithm and the proposed algorithm are applied to a quadruped robot;walking performances are then compared by gait *** the two algorithms exhibit similar computation rates,the proposed algorithm shows better fitness and a wider search *** evolutionary tendency of the walking trajectory is analyzed using the optimized results,and the findings provide insight into reliable leg trajectory design.
Image segmentation denotes a process by which a raw input image is partitioned into nonoverlapping regions such that each region is homogeneous and the union of any two adjacent regions is heterogeneous. A segmented i...
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Image segmentation denotes a process by which a raw input image is partitioned into nonoverlapping regions such that each region is homogeneous and the union of any two adjacent regions is heterogeneous. A segmented image is considered to be the highest domain-independent abstraction of an input image. The image segmentation problem is treated as one of combinatorial optimization. A cost function which. incorporates both edge information and region gray-scale uniformity is defined. The cost function is shown to be multivariate with several local minima. The genetic algorithm, a stochastic optimization technique based on evolutionary computation, is explored in the context of image segmentation. A class of hybrid evolutionary optimization algorithms based on a combination of the genetic algorithm and stochastic annealing algorithms such as simulated annealing, microcanonical annealing, and the random cost algorithm is shown to exhibit superior performance as compared with the canonical genetic algorithm. Experimental results on gray-scale images are presented.
This paper presents a small sample of evidences of the direct and clear influence of the Darwin's Theory of Evolution on the Computer Science field, putting the core seed of the well-known evolutionary computation...
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This paper presents a small sample of evidences of the direct and clear influence of the Darwin's Theory of Evolution on the Computer Science field, putting the core seed of the well-known evolutionary computation and making Computer Science overcome some previous algorithmic limitations. The paper also shows how the more faithful to the Evolution Theory the algorithms, the better their performance and robustness, thus uncovering the crucial importance of the ideas collected in for the development of computation and, indirectly through this, for the development of a great diversity of knowledge areas.
I have had the privilege of involvement in this field from its early days. The result is a rather unique and comprehensive perspective on its development and growth. In this talk I use that perspective to highlight so...
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ISBN:
(纸本)9798400701191
I have had the privilege of involvement in this field from its early days. The result is a rather unique and comprehensive perspective on its development and growth. In this talk I use that perspective to highlight some important milestones, discuss some current issues and suggest some directions for the future.
The learning classifier system (LCS) integrates a rule-based system with reinforcement learning and genetic algorithm-based rule discovery. This investigation reports on the design, implementation, and evaluation of E...
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The learning classifier system (LCS) integrates a rule-based system with reinforcement learning and genetic algorithm-based rule discovery. This investigation reports on the design, implementation, and evaluation of EpiCS, a LCS adapted for knowledge discovery in epidemiologic surveillance. Using data from a large, national child automobile passenger protection program, EpiCS was compared with C4.5 and logistic regression to evaluate its ability to induce rules from data that could be used to classify cases and to derive estimates of outcome risk, respectively. The rules induced by EpiCS were less parsimonious than those induced by C4.5, but were potentially more useful to investigators in hypothesis generation. Classification performance of C4.5 was superior to that of EpiCS (P < 0.05). However, risk estimates derived by EpiCS were significantly more accurate than those derived by logistic regression (P < 0.05). (C) 2000 Elsevier Science B.V. All rights reserved.
An evolutionary computation algorithm known as genetic programming (GP) has been explored as an alternative tool for improving the ensemble forecast of 24-h accumulated precipitation. Three GP versions and six ensembl...
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An evolutionary computation algorithm known as genetic programming (GP) has been explored as an alternative tool for improving the ensemble forecast of 24-h accumulated precipitation. Three GP versions and six ensembles' languages were applied to several real-world datasets over southern, southeastern and central Brazil during the rainy period from October to February of 2008-2013. According to the results, the GP algorithms performed better than two traditional statistical techniques, with errors 27-57% lower than simple ensemble mean and the MASTER super model ensemble system. In addition, the results revealed that GP algorithms outperformed the best individual forecasts, reaching an improvement of 34-42%. On the other hand, the GP algorithms had a similar performance with respect to each other and to the Bayesian model averaging, but the former are far more versatile techniques. Although the results for the six ensembles' languages are almost indistinguishable, our most complex linear language turned out to be the best overall proposal. Moreover, some meteorological attributes, including the weather patterns over Brazil, seem to play an important role in the prediction of daily rainfall amount.
This study presents a comparative study for four evolutionary computation (EC) methods to the optimal active-reactive power dispatch (ARPD) problem. Theoretically, there is a coupling relation between ARPDs. However, ...
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This study presents a comparative study for four evolutionary computation (EC) methods to the optimal active-reactive power dispatch (ARPD) problem. Theoretically, there is a coupling relation between ARPDs. However, because of high X/R ratio existing in the transmission line, the problem of ARPD can be decomposed into two individual sub-problems by the decoupling concept, that is, ARPD problems. In this study, the evolutionary programming (EP), particle swarm optimisation (PSO), differential evolution (DE) and the proposed hybrid differential evolution (HDE) algorithms are used to separately solve the ARPD problem. To evaluate the performance of each method, the IEEE 30-bus and Taiwan Power Company (TPC) 345 kV simplified systems are employed as the study cases. The results indicate that the proposed HDE can obtain better results than the other methods in terms of active power transmission losses, voltage deviation, operating cost and convergence performance.
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