The paper addresses key aspects of our experience with evolutionary computation techniques as applied to dealing with the problems of operation planning and expansion planning. Distribution planning - which includes o...
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ISBN:
(纸本)0780372859
The paper addresses key aspects of our experience with evolutionary computation techniques as applied to dealing with the problems of operation planning and expansion planning. Distribution planning - which includes operation planning and expansion planning - can be made optimal by evolutionary computation. The interchange path algorithm plays a major role in the process. The benefits of optimal planning in increasing capacity, improving efficiency and reliability, and in selecting investments are reported.
The architecture of an artificial neural network has a significant influence on its performance. For a given problem, the proper architecture is found by trial and error. This approach is time consuming and may not al...
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ISBN:
(纸本)0819444898
The architecture of an artificial neural network has a significant influence on its performance. For a given problem, the proper architecture is found by trial and error. This approach is time consuming and may not always produce the optimal network. In this reason, we can apply the evolutionary computation such as genetic algorithm to implement the automation of network's structure as well as the biological inspiration in neural networks to successfully adapt varying input environment. Moreover, we examine the performance of combining multiple evolving networks that are more likely to model the neurophysiology of the human brain. In the combining module, all individual networks or selected individual networks in the population are used. Also, the dynamic data set is used to provide the networks with diversity and generalization. In this model, each evolving individual network is designed to have a specific feature set and neuron connection links for given data. Then, the results are combined through the combining module to improve the generalization performance of the single network. The Iris and Austrian credit data are used in the experiment.
This paper proposes an improved computational algorithm for structure topology optimization. It integrates the merits of evolutionary Structure Optimization and Level Set Method (LSM) for structure topology optimizati...
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ISBN:
(纸本)9780878492459
This paper proposes an improved computational algorithm for structure topology optimization. It integrates the merits of evolutionary Structure Optimization and Level Set Method (LSM) for structure topology optimization. Traditional LSM algorithm has some drawbacks, for instance, its optimal topology configuration is largely dependent on the structural topology initialization. Additionally, new holes cannot be evolved within the updated topology during the optimization iteration. The method proposed in this paper combines the merits of ESO techniques with the LSM scheme, allowing new holes to be automatically inserted in regions with low deformation energy at prescribed iterations of the optimization. The nodal neighboring region is a good selection. For complex structures in which holes cannot be properly inserted in advance, the proposed method considerably improves the ability of LSM to search the optimal topology. In addition to achieving more accurate results, the proposed method also yields higher efficiency during optimization. Benchmark problems are presented to show the effectiveness and robustness of the new proposed algorithm.
This paper examines the use of evolutionary computation (EC) find optimal solution in vehicle assignment problem (VAP) The VAP refers to the allocation of the expected number of people in a potentially flooded;ilea to...
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ISBN:
(数字)9783642134951
ISBN:
(纸本)9783642134944
This paper examines the use of evolutionary computation (EC) find optimal solution in vehicle assignment problem (VAP) The VAP refers to the allocation of the expected number of people in a potentially flooded;ilea to various types of available vehicles in evacuation process A novel discrete particle swami optimization (DPSO) algorithm and genetic algorithm (GA) are presented to solve this problem. Both of these algorithms employed a discrete solution representation and incorporated a min-max approach for a random initialization of discrete particle position A min-max approach is based on minimum capacity and maximum capacity of vehicles We analyzed the performance of the algorithms using evacuation (datasets The quality of solutions were measured a based on the objective function which is to find a maximum number of assumed people to vehicles in the potentially flooded areas and central processing.: unit (CPU) processing time of the algorithms Overall. DPSO provides an optimal SO I ut ions and successfully achieved the objective function whereas GA gives sub optimal solution for the VAP
This paper presents a review of our recently completed interdisciplinary research project "evolutionary Perspective on Collective Decision Making" that was conducted through close collaboration between compu...
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ISBN:
(纸本)9781467358637
This paper presents a review of our recently completed interdisciplinary research project "evolutionary Perspective on Collective Decision Making" that was conducted through close collaboration between computational, organizational and social scientists at Binghamton University. In this project, we utilized evolutionary computation in several non-traditional ways-(1) as a theoretical framework for reinterpreting the dynamics of collective human decision making processes, (2) as a computational simulation model of idea generation and selection, and (3) as a research tool for collecting high-resolution experimental data of actual collaborative design and decision making from human subjects.
Biologists have developed models to explain why different environmentally induced morphs of the same organism exist over time. Such conditional strategies are a common form of adaptation to variable environments, wher...
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ISBN:
(纸本)1595930108
Biologists have developed models to explain why different environmentally induced morphs of the same organism exist over time. Such conditional strategies are a common form of adaptation to variable environments, whereby an environmental cue allows some individuals to respond to the cue and develop into a morph that is different from the morph of individuals that do not receive the cue. Recently, these efforts have resulted in two different analytical models that give somewhat different predictions. Here we apply evolutionary computation methods to test the two analytical models. The results bear a remarkable similarity to the results of one of the two analytical models. The paper that follows presents the details of a biological application involving snails and barnacles (that occur naturally in two different morphs), moving then to an explanation of two competing mathematical models of the application. Finally, the interdisciplinary paper, which coordinates three separate research projects of a biologist, a mathematician and a computer scientist, describes the evolutionary computation methods used to support one of the two competing analytical models.
This volume encloses research articles that were presented at the EVOLVE 2014 International Conference in Beijing, China, July 14, 2014. The book gathers contributions that emerged from the conference tracks, ranging ...
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ISBN:
(数字)9783319074948
ISBN:
(纸本)9783319074931;9783319074948
This volume encloses research articles that were presented at the EVOLVE 2014 International Conference in Beijing, China, July 14, 2014. The book gathers contributions that emerged from the conference tracks, ranging from probability to set oriented numerics and evolutionary computation; all complemented by the bridging purpose of the conference, e.g. Complex Networks and Landscape Analysis, or by the more application oriented perspective. The novelty of the volume, when considering the EVOLVE series, comes from targeting also the practitioners view. This is supported by the Machine Learning Applied to Networks and Practical Aspects of evolutionary Algorithms tracks, providing surveys on new application areas, as in the networking area and useful insights in the development of evolutionary techniques, from a practitioners perspective. Complementary to these directions, the conference tracks supporting the volume, follow on the individual advancements of the subareas constituting the scope of the conference, through the computational Game Theory, Local Search and Optimization, Genetic Programming, evolutionary Multi-objective optimization tracks.
In this paper, an algorithm for many-objective evolutionary computation, which is based on the NSGA-II with the Chebyshev preference relation, is applied to multi-objective design optimization problem of dielectric ba...
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ISBN:
(纸本)9781479914883
In this paper, an algorithm for many-objective evolutionary computation, which is based on the NSGA-II with the Chebyshev preference relation, is applied to multi-objective design optimization problem of dielectric barrier discharge plasma actuator (DBDPA). The present optimization problem has four design parameters and six objective functions. The main goal of the paper is to extract useful design guidelines to predict control flow behavior based on the DBDPA parameter values using the resulting approximation Pareto set obtained by the optimization.
Feature subset selection is an optimization problem to achieve high classification accuracy with low number of features and low computational cost in the area of pattern classification or data mining. There are variou...
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ISBN:
(纸本)9781538658260
Feature subset selection is an optimization problem to achieve high classification accuracy with low number of features and low computational cost in the area of pattern classification or data mining. There are various approaches to obtain this. Basically a search algorithm is used with a fitness function either based on intrinsic characteristics of the data, known as filter type, or based on classification accuracy of the classifier used, known as the wrapper type, to find out the optimum feature subset. Both the approaches have respective merits and demerits. Though lots of algorithms are developed so far, none of them works equally well for all the data sets, specially for very high dimensional data sets. In this work, a new feature evaluation measure based on the concept borrowed from topic modelling in text processing, has been developed. The proposed measure is used as a fitness function of evolutionary computational search techniques for designing filter type feature subset selection approach. Simulation experiments with various benchmark data sets have been done for assessing the efficiency of the proposed approach in comparison to the popular conventional filter type feature selection algorithms mRMR and CFS. It is found that the proposed approach is better in terms of selecting lesser number of features with comparable classification accuracy. The proposed algorithms work better for higher dimensional features and can be proved as an effective solution of feature selection for very high dimensional data.
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