10632351
Flexible ligand docking using a robust evolutionary algorithm
A flexible ligand docking protocol based on evolutionary algorithms is investigated. The proposed approach incorporates family competition and adaptive rules to integrate decreasing-based mutations and self-adaptive mutations to act as global and local search strategies, respectively. The method is applied to a dihydrofolate reductase enzyme with the anticancer drug methotrexate and two analogues of antibacterial drug trimethoprim. Conformations and orientations closed to the crystallographically determined structures are obtained, as well as alternative structures with low energy. Numerical results indicate that the new approach is very robust. The docked lowest-energy structures have root-mean-square derivations ranging from 0.67 to 1.96 Å with respect to the corresponding crystal structures. c © 2000 John Wiley & Sons, Inc. J Comput Chem 21: 988–998, 2000
40205984
A Robust Evolutionary Algorithm for Training Neural Networks
A new evolutionary algorithm is introduced for training both feedforward and recurrent neural networks. The proposed approach, called the Family Competition Evolutionary Algorithm (FCEA), automatically achieves the balance of the solution quality and convergence speed by integrating multiple mutations, family competition and adaptive rules. We experimentally analyse the proposed approach by showing that its components can cooperate with one another, and possess good local and global properties. Following the description of implementation details, our approach is then applied to several benchmark problems, including an artificial ant problem, parity problems and a two-spiral problem. Experimental results indicate that the new approach is able to stably solve these problems, and is very competitive with the comparative evolutionary algorithms.
5717822
A family competition evolutionary algorithm for automated docking of flexible ligands to proteins
We study an evolutionary algorithm for flexible ligand docking. Based on family competition and adaptive rules, the proposed approach consists of global and local strategies by integrating decreasing mutations and self-adaptive mutations. To demonstrate the robustness of the proposed approach, we apply it to the problems of the first international contests on evolutionary optimization. Following the description of function optimization, our approach is applied to a dihydrofolate reductase enzyme with the anti-cancer drug methotrexate and with two analogs of the antibacterial drug trimethoprim. Our numerical results indicate that the proposed approach is robust. The docked lowest energy structures have rms derivations ranging from 0.72 A to 1.98 A with respect to the corresponding crystal structure.
5502812
A Continuous Genetic Algorithm for Global Optimization
207007399
Integrating adaptive mutations and family competition into genetic algorithms as function optimizer
Abstract In this paper, we propose a robust evolutionary algorithm, called adaptive mutations genetic algorithm, for function optimization problems. Our main contribution is robustly optimizing problems whose number of variables from 2 to 200. In order to have a fair comparison, we propose the criteria for constructing a testing bed and for classifying these problems into different complexity degrees. The proposed approach, based on the family competition and multiple adaptive rules, successfully integrates the decreasing-based Gaussian mutation and self-adaptive Cauchy mutation to balance the exploitation and exploration. It is implemented and applied to widely used test functions and several nonseparable multimodal functions. Experimental results indicate that our approach is more robust than ten evolutionary algorithms.
26384016
A new evolutionary approach to developing neural autonomous agents
This paper explores the use of neural networks to control robots in tasks requiring sequential and learning behavior. We propose a family competition evolutionary algorithm (FCEA) to evolve networks that can integrate these different types of behavior in a smooth and continuous manner. The approach integrates self-adaptive Gaussian mutation, self-adaptive Cauchy mutation, decreasing-based Gaussian mutation, and family competition. In order to illustrate the power of the approach, we apply this approach to two different task domains: the "artificial ant" problem and a sequential behavior problem - an agent learns to play football. From the experimental results, we find our approach performs much better than other evolutionary algorithms in these two tasks. Based on the results from our experiments, it is shown that our approach can evolve neural networks to provide a means of integrating, sequencing and learning within a single control system.
2345378
Segmentation of 3D deformable objects with level set based prior models
We propose a level set based deformable model for the segmentation of multiple objects from 3D medical images using shape prior constraints. As an extension to the level set distribution model of object shape presented in previous papers, this paper evaluates the performance of the level set representation of the object shape by comparing it with the point distribution model(PDM) using the Chi-square test. We define a maximum a posteriori (MAP) estimation model using level set based prior information to realize the segmentation of the multiple objects. To achieve this , only one level set function is employed as the representation of the multiple objects of interest within the image. We then define the probability distribution over the variations of objects contained in a set of training images. We found the algorithm to be computationally efficient, robust to noise, able to handle multidimensional data, and avoids the need for explicit point correspondences during the training phase. Results and validation from various experiments on 2D/3D medical images are demonstrated.
8185115
On the combination and normalization of interval-valued belief structures
This paper investigates the issues of combination and normalization of interval-valued belief structures within the framework of Dempster-Shafer theory (DST) of evidence. Existing approaches are reviewed, examined and critically analysed. They either ignore the normalization or separate it from the combination process, leading to irrational or suboptimal interval-valued belief structures. A new logically correct optimality approach is developed, where the combination and the normalization are optimised together rather than separately. Numerical examples are provided throughout the paper.
26719731
Recursive discrete Fourier transform with unified IIR filter structures
In this paper, we propose two unified IIR filters to recursively compute the discrete Fourier transform (DFT). With the advantage of local connection, regularity and modularity, the proposed methods can compute all DFT coefficients with the same filter structure. Based on stability behavior, the proposed IIR filter structures with optimal coefficients can achieve more accurate results than the traditional ones whose filter coefficients should be changed for computing the different DFT coefficients. Simulation results verify the aforementioned theoretical observations.
11796157
Functional Brain Image Analysis Using Joint Function-Structure Priors
We propose a new method for context-driven analysis of functional magnetic resonance images (fMRI) that incorporates spatial relationships between functional parameter clusters and anatomical structure directly for the first time. We design a parametric scheme that relates functional and structural spatially-compact regions in a single unified manner. Our method is motivated by the fact that the fMRI and anatomical MRI (aMRI) have consistent relations that provide configurations and context that aid in fMRI analysis. We develop a statistical decision-making strategy to estimate new fMRI parameter images (based on a General Linear Model-GLM) and spatially-clustered zones within these images. The analysis is based on the time-series data and contextual information related to appropriate spatial grouping of parameters in the functional data and the relationship of this grouping to relevant gray matter structure from the anatomical data. We introduce a representation for the joint prior of the functional and structural information, and define a joint probability distribution over the variations of functional clusters and the related structure contained in a set of training images. We estimate the Maximum A Posteriori (MAP) functional parameters, formulating the function-structure model in terms of level set functions. Results from 3D fMRI and aMRI show that this context-driven analysis potentially extracts more meaningful information than the standard GLM approach.
8764549
Efficient rate-distortion estimation for H.264/AVC coders
In video coders, the optimal coding mode decision for each coding block can be achieved by exhaustively calculating the rate-distortion cost, which simultaneously considers the distortion performance and the coding bit consumption of all possible modes. The best mode is chosen from the one with the minimum Lagrange cost. To avoid the expensive computation of Lagrange costs, in this paper, we propose transform-domain bit-rate estimation and distortion measures, based on quantized and inverse quantized integer transform coefficients, for the inter-mode decision in H.264/AVC coders. With the proposed scheme, entropy coding, inverse transform, and pixel-reconstructions are not required in the process. With ignorable degradation in coding performance, simulations demonstrate that the proposed estimation method achieves about 40% reduced computation time of rate-distortion cost for the inter-mode decision and saves about 17% total encoding time while combining with fast motion estimation and fast mode decision algorithms.