Conductance-based compartmental neuron models are traditionally used to investigate the electrophysiological properties of neurons. These models require a number of parameters to be adjusted to biological experimental...
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ISBN:
(纸本)9783642122101
Conductance-based compartmental neuron models are traditionally used to investigate the electrophysiological properties of neurons. These models require a number of parameters to be adjusted to biological experimental data and this question can be posed as an optimization problem. In this paper we investigate the behavior of different estimation of distribution algorithms (EDAs) for this problem. We focus on studying the influence that ate interactions between the neuron model conductances have in the complexity of the optimization problem. We support evidence that the use of these interactions during the optimization process can improve the EDA behavior.
Probabilistic model-building algorithms (PMBGAs) replace traditional variation of genetic and evolutionary algorithms by (1) building a probabilistic model of promising solutions and (2) sampling the built model to ge...
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ISBN:
(纸本)9781450300735
Probabilistic model-building algorithms (PMBGAs) replace traditional variation of genetic and evolutionary algorithms by (1) building a probabilistic model of promising solutions and (2) sampling the built model to generate new candidate solutions. PMBGAs are also known as estimation of distribution algorithms (EDAs) and iterated density-estimationalgorithms (IDEAs).Replacing traditional crossover and mutation operators by building and sampling a probabilistic model of promising solutions enables the use of machine learning techniques for automatic discovery of problem regularities and exploitation of these regularities for effective exploration of the search space. Using machine learning in optimization enables the design of optimization techniques that can automatically adapt to the given problem. There are many successful applications of PMBGAs, for example, Ising spin glasses in 2D and 3D, graph partitioning, MAXSAT, feature subset selection, forest management, groundwater remediation design, telecommunication network design, antenna design, and *** tutorial Probabilistic Model-Building GAs will provide a gentle introduction to PMBGAs with an overview of major research directions in this area. Strengths and weaknesses of different PMBGAs will be discussed and suggestions will be provided to help practitioners to choose the best PMBGA for their problem.
In computer numerical control (CNC) machining problems, it is important to reduce the production cost To deal with the nonlinear optimization problem of machining parameters which aims to minimize the unit production ...
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ISBN:
(纸本)9781424447541
In computer numerical control (CNC) machining problems, it is important to reduce the production cost To deal with the nonlinear optimization problem of machining parameters which aims to minimize the unit production cost (UC) in multi-pass turning operations, two estimation of distribution algorithms (EDAs) incorporated with gene repair method are proposed to search the optimal solution for machining parameters Computer simulation results show that the proposed algorithms are efficient in searching the optimal machining parameters, which significantly reduce the unit production cost
In many optimization problems, regardless of the domain to which it belongs, the structural component that the interactions among variables provides can be seen as a network. The impact that the topological characteri...
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ISBN:
(纸本)9781424481262
In many optimization problems, regardless of the domain to which it belongs, the structural component that the interactions among variables provides can be seen as a network. The impact that the topological characteristics of that network has, both in the hardness of the problem and in the performance of the optimization techniques, constitutes a very important subject of research. In this paper, we study the behavior of estimation of distribution algorithms (EDAs) in functions whose structure is defined by using different network topologies which include grids, small-world networks and random graphs. In order to do that, we use several descriptors such as the population size, the number of evaluations as well as the structures learned during the search. Furthermore, we take measures from the field of complex networks such as clustering coefficient or characteristic path length in order to quantify the topological properties of the function structure and analyze their relation with the behavior of EDAs. The results show that these measures are useful to have better understanding of this type of algorithms which have exhibited a high sensitivity to the topological characteristics of the function structure. This study creates a link between EDAs based on Bayesian networks and the emergent field of complex networks.
This paper studies the probabilistic based evolutionary algorithms in dealing with bi-objective travelling salesman problem. Multi-objective restricted Boltzmann machine and univariate marginal distributionalgorithm ...
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ISBN:
(纸本)9783642172977
This paper studies the probabilistic based evolutionary algorithms in dealing with bi-objective travelling salesman problem. Multi-objective restricted Boltzmann machine and univariate marginal distributionalgorithm in binary representation are modified into permutation based representation. Each city is represented by an integer number and the probability distributions of the cities are constructed by running the modeling approach. A refinement operator and a local exploitation operator are proposed in this work. The probabilistic based evolutionary optimizers are subsequently combined with genetic based evolutionary optimizer to complement the limitations of both algorithms.
In this paper,we design a hybrid multi-objective algorithm using genetic and estimation of distribution based on design of *** first,we apply orthogonal design and uniform design to generate an initial population so t...
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In this paper,we design a hybrid multi-objective algorithm using genetic and estimation of distribution based on design of *** first,we apply orthogonal design and uniform design to generate an initial population so that the population individual solutions scattered evenly in the feasible solutions ***,we proposed a new convergence criterion to check whether the distribution of population has the obvious *** the population is convergence,we use the model-based method to reproduce new individual solutions,otherwise genetic operator was employed to generate *** results of systematic experiments show that the hybrid algorithm this paper proposed capable of finding much better convergence near the Pareto-optimal solutions and better spread of solutions than RM-MEDA.
estimation of distribution algorithm (EDA) is a new evolutionary computation method based on probabilistic theory. EDA can select optimal individuals through estimating probability distribution function of a populatio...
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ISBN:
(纸本)9781424467129
estimation of distribution algorithm (EDA) is a new evolutionary computation method based on probabilistic theory. EDA can select optimal individuals through estimating probability distribution function of a population. The capture problem among multi software robots can be solved by EDA. The capture problem involves that some pursuers pursue several evaders through part of trajectory. The trajectory was produced by the evaders during their two-dimensional random mobility. The pursuers estimate the evaders' mobility functions and adjust their pursuit models to capture the evaders as fast as possible. The probabilistic evolutionary courses of multi-robot experiencing some competitions are analyzed in performances. The analysis shows that capture problem of multi-robot solved by EDA is better than other methods in several aspects.
In computer numerical control(CNC) machining problems,it is important to reduce the production *** deal with the nonlinear optimization problem of machining parameters which aims to minimize the unit production cost(U...
详细信息
In computer numerical control(CNC) machining problems,it is important to reduce the production *** deal with the nonlinear optimization problem of machining parameters which aims to minimize the unit production cost(UC) in multi-pass turning operations,two estimation of distribution algorithms(EDAs) incorporated with gene repair method are proposed to search the optimal solution for machining *** simulation results show that the proposed algorithms are efficient in searching the optimal machining parameters,which significantly reduce the unit production cost.
This paper proposes a combination of differential evolution (DE) and estimation of distribution algorithm (EDA) to design photonic crystal fiber structures with desired properties over the C communication band. In ord...
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This paper proposes a combination of differential evolution (DE) and estimation of distribution algorithm (EDA) to design photonic crystal fiber structures with desired properties over the C communication band. In order to determine the effective index of propagation of the mode and then, the other properties of structure, a finite difference frequency domain (FDFD) solver is applied. The results revealed that the proposed method is a powerful tool for solving this optimization problem. The optimized PCF exhibits a dispersion of 0.22 ps nm-1 km-1 at 1.55 mu m wavelength with a variance of +/- 0.4 ps nm-1 km-1 over the C communication band and a nearly zero dispersion slope.
This paper proposes a combination of differential evolution (DE) and estimation of distribution algorithm (EDA) to design photonic crystal fiber structures with desired properties over the C communication band. In ord...
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This paper proposes a combination of differential evolution (DE) and estimation of distribution algorithm (EDA) to design photonic crystal fiber structures with desired properties over the C communication band. In order to determine the properties of PCFs such as dispersion, dispersion slope and loss, an artificial intelligence method, the Nero-Fuzzy system, is applied. In addition, a special cost function which simultaneously includes the confinement loss, dispersion and its slope is used in the proposed design approach. The results revealed that the proposed method is a powerful tool for solving this optimization problem. The optimized PCF exhibits an ultra low confinement loss and low dispersion at 1.55 mu m wavelength with a nearly zero dispersion slope over the C communication band.
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