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1.
I. Belda X. Llorà E. Giralt 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2006,10(4):295-304
One of the goals of computational chemistry is the automated de novo design of bioactive molecules. Despite significant progress
in computational approaches to ligand design and efficient evaluation of binding energy, novel procedures for ligand design
are required. Evolutionary computation provides a new approach to this design issue. This paper presents an automated methodology
for computer-aided peptide design based on evolutionary algorithms. It provides an automatic tool for peptide de novo design,
based on protein surface patches defined by user. Regarding the restrictive constrains of this problem a special emphasis has been made on the design of the
evolutionary algorithms implemented. 相似文献
2.
Carlos Fernandes Agostinho C. Rosa 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2008,12(10):955-979
Mate selection plays a crucial role in both natural and artificial systems. While traditional Evolutionary Algorithms (EA)
usually engage in random mating strategies, that is, mating chance is independent of genotypic or phenotypic distance between
individuals, in natural systems non-random mating is common, which means that somehow this mechanism has been favored during
the evolutionary process. In non-random mating, the individuals mate according to their parenthood or likeness. Previous studies
indicate that negative assortative mating (AM)—also known as dissortative mating—, which is a specific type of non-random mating, may improve EAs performance by maintaining the genetic diversity of the
population at a higher level during the search process. In this paper we present the Variable Dissortative Mating Genetic Algorithm (VDMGA). The algorithm holds a mechanism that varies the GA’s mating restrictions during the run by means of simple rule
based on the number of chromosomes created in each generation and indirectly influenced by the genetic diversity of the population.
We compare VDMGA not only with traditional Genetic Algorithms (GA) but also with two preceding non-random mating EAs: the
CHC algorithm and the negative Assortative Mating Genetic Algorithm (nAMGA). We intend to study the effects of the different methods in the performance of GAs and verify the reliability of
the proposed algorithm when facing an heterogeneous set of landscapes. In addition, we include the positive Assortative Mating Genetic Algorithm (pAMGA) in the experiments in order test both negative and positive AM mechanisms, and try to understand if and when negative
AM (or DM) speeds up the search process or enables the GAs to escape local optima traps. For these purposes, an extensive
set of optimization test problems was chosen to cover a variety of search landscapes with different characteristics. Our results
confirm that negative AM is effective in leading EAs out of local optima traps, and show that the proposed VDMGA is at least
as efficient as nAMGA when applied to the range of our problems, being more efficient in very hard functions were traditional
GAs usually fail to escape local optima. Also, scalability tests have been made that show VDMGA ability to decrease optimal
population size, thus reducing the amount of evaluations needed to attain global optima. We like to stress that only two parameters
need to be hand-tuned in VDMGA, thus reducing the tuning effort present in traditional GAs and nAMGA. 相似文献
3.
Otis Smart Hiram Firpi George Vachtsevanos 《Engineering Applications of Artificial Intelligence》2007,20(8):1070-1085
This paper presents an application of genetic programming (GP) to optimally select and fuse conventional features (C-features) for the detection of epileptic waveforms within intracranial electroencephalogram (IEEG) recordings that precede seizures, known as seizure precursors. Evidence suggests that seizure precursors may localize regions important to seizure generation on the IEEG and epilepsy treatment. However, current methods to detect epileptic precursors lack a sound approach to automatically select and combine C-features that best distinguish epileptic events from background, relying on visual review predominantly. This work suggests GP as an optimal alternative to create a single feature after evaluating the performance of a binary detector that uses: (1) genetically programmed features; (2) features selected via GP; (3) forward sequentially selected features; and (4) visually selected features. Results demonstrate that a detector with a genetically programmed feature outperforms the other three approaches, achieving over 78.5% positive predictive value, 83.5% sensitivity, and 93% specificity at the 95% level of confidence. 相似文献
4.
The problem of determining the maximum mean response level crossing rate of a linear system driven by a partially specified Gaussian load process has been considered. The partial specification of the load is given only in terms of its total average energy. The critical input power spectral (PSD) function, which maximizes the mean response level crossing rate, is obtained. The critical input PSD turns out to be highly narrow-banded which fails to capture the erratic nature of the excitation. Consequently, the trade-off curve between the maximum mean response level crossing rate and the maximum disorder in the input process, quantified in terms of its entropy rate, has been generated. The method of Pareto optimization is used to tackle the conflicting objectives of the simultaneous maximization of the mean response level crossing rate and the input entropy rate. The non-linear multi-objective optimization has been carried out using a recently developed multi-criteria genetic algorithm scheme. Illustrative example of determining the critical input of an axially vibrating rod, excited by a partially specified stationary Gaussian load process, has been considered. 相似文献
5.
Data fitting with a spline using a real-coded genetic algorithm 总被引:2,自引:0,他引:2
Fujiichi Yoshimoto Author Vitae Toshinobu Harada Author Vitae Author Vitae 《Computer aided design》2003,35(8):751-760
To obtain a good approximation for data fitting with a spline, frequently we have to deal with knots as variables. The problem to be solved then becomes a continuous nonlinear and multivariate optimization problem with many local optima. Therefore, it is difficult to obtain the global optimum. In this paper, we propose a method for solving this problem by using a real-coded genetic algorithm. Our method can treat not only data with a smooth underlying function, but also data with an underlying function having discontinuous points and/or cusps. We search for the best model among candidate models by using the Bayes Information Criterion (BIC). With this, we can appropriately determine the number and locations of knots automatically and simultaneously. Five examples of data fitting are given to show the performance of our method. 相似文献
6.
7.
目的制备新生牛肝活性肽并评价其安全性。方法采用膜法分离制备新生牛肝活性肽。将3批制品于37~40℃,75%相对湿度条件下存放3个月,以多肽含量为指标观察其稳定性,并进行急性毒性试验、小鼠骨髓细胞微核试验、Ames试验、小鼠精子畸形试验和大鼠喂养试验。结果制备的3批新生牛肝活性肽存放3个月后,多肽含量无明显下降,质量稳定。小鼠和大鼠经口灌人大于20.0g/kg体重的新生牛肝活性肽,均无急性毒性。3种致突变试验均未显示出致突变性,大鼠喂养试验各项指标均未见明显毒性。结论新生牛肝活性肽未表现出明显毒性。 相似文献
8.
Celso M. F. Lapa Cludio M. N. A. Pereira Paulo F. Frutuoso e Melo 《Reliability Engineering & System Safety》2003,81(1):103-109
In order to maximize systems average availability during a given period of time, it has recently been developed a non-periodic surveillance test optimization methodology based on genetic algorithms (GA). The fact of allowing non-periodic tests turns the solution space much more flexible and schedules can be better adjusted, providing gains in the overall system average availability, when compared to those obtained by an optimized periodic test scheme. This approach, however, turns the optimization problem more complex. Hence, the use of a powerful optimization technique, such as GA, is required.Considering that some particular features of certain systems can turn it advisable to introduce other specific constraints in the optimization problem, this work investigates the application of seasonal constraints for the set of the Emergency Diesel Generation of a typical four-loop pressurized water reactor in order to planning and optimizing its surveillance test policy. In this analysis, the growth of the blackout accident probability during summer, due to electrical power demand increases, was considered. Here, the used model penalizes surveillance test interventions when the blackout probability is higher.Results demonstrate the ability of the method in adapting the surveillance test policy to seasonal constraints. The knowledge acquired by the GA during the searching process has lead to test schedules that drastically minimize test interventions at periods of high blackout probability. It is compensated by more frequent redistributed tests through the periods of low blackout probability in order to improve on the overall average availability at the system level. 相似文献
9.
Transformations in machining. Part 1. enhancement of wavelet transformation neural network (WT-NN) combination with a preprocessor 总被引:2,自引:0,他引:2
X. Wang P. Chen I.N. Tansel Yenilmez A 《International Journal of Machine Tools and Manufacture》2006,46(1):36-42
Properly selected transformation methods obtain the most significant characteristics of metal cutting data efficiently and simplify the classification. Wavelet Transformation (WT) and Neural Networks (NN) combination was used to classify the experimental cutting force data of milling operations previously. Preprocessing (PreP) of the approximation coefficients of the WT is proposed just before the classification by using the Adaptive Resonance Theory (ART2) type NNs. Genetic Algorithm (GA) was used to estimate the weights of each coefficient of the PreP. The WT-PreP-NN (ART2) combination worked at lower vigilances by creating only a few meaningful categories without any errors. The WT-NN (ART2) combination could obtain the same error rate only if very high vigilances are used and many categories are allowed. 相似文献
10.