Effective GA approach for a direct evaluation of reaction kinetic within EPDM accelerated sulphur crosslinking

A direct genetic algorithm (GA) approach with kinetic base, to provide effective numerical estimates of vulcanization level for EPDM cross-linked with accelerated sulphur is presented. The model requires a preliminary characterization of rubber through standard rheometer tests. A recently presented kinetic exponential model is used as starting point to develop the algorithm proposed. In such a model, three kinetic constants have to be determined by means of a non-linear least-squares curve fitting. The approach proposed circumvents a sometimes inefficient and not convergent non-linear data fitting, disregarding at a first attempt reversion and finding the local minimum of a suitable two-variable error function, to have an estimate of the first two kinetic constants. A comparison between present GA approach and traditional gradient based algorithms is discussed. The last constant, representing reversion is again evaluated through a minimization performed on a single variable error function. The applicability of the approach is immediate and makes the model extremely appealing when fast and reliable estimates of crosslinking density of cured EPDM are required. To show the capabilities of the approach proposed, a comprehensive comparison with both available experimental data and results obtained numerically with a least square exponential model for a real compound at different temperatures is provided.     

Elastic modulus of free-standing lipid bilayer

In the current study, molecular dynamics (MD), finite element (FE) method, and genetic algorithm are employed to compute Young’s modulus of free-standing DPPC lipid bilayer. MD method is utilized to simulate loading of a free-standing DPPC lipid bilayer under an indenter. Indentation experiment is also simulated with FE method where genetic algorithm controls value of Young’s modulus in FE simulation and finds the best value for it. The best value means the value results in a force–depth curve which agrees well with the curve obtained from MD simulation. While simulating indentation with MD method two distinct regimes are distinguished in force–depth curve before rupture of the bilayer. The first regime shows elastic response of the bilayer to indentation and it is shown that force–depth curve can be fitted with a cubic polynomial in this regime. The second regime starts at the point which the force–depth curve changes from convex to concave. This point is an inflection point and would be regarded as yield point of the bilayer. Slope of the curve decreases with indentation depth in this regime which shows changes in internal structure of the bilayer. Also we investigate effects of indenter’s shape and indentation speed on computed Young’s modulus and show rate-dependent behavior of free-standing lipid bilayer.     

A hybrid genetic algorithm for estimating the equilibrium potential of an ion-selective electrode

Non-linear equations can be used to model the measured potential of ion-selective electrodes (ISEs) as a function of time. This can be done by using non-linear least squares regression to fit parameters of non-linear equations to an ISE response curve. In iterative non-linear least squares regression (which can be considered as local optimisers), the determination of starting parameter estimates that yield convergence to the global optimum can be difficult. Starting values away from the global optimum can lead to either abortive divergence or convergence to a local optimum. To address this issue, a global optimisation technique was used to find initial parameter estimates near the global optimum for subsequent further refinement to the absolute optimum. A genetic algorithm has been applied to two non-linear equations relating the measured potential from selected ISEs to time. The parameter estimates found from the genetic algorithm were used as starting values for non-linear least squares regression, and subsequent refinement to the absolute optimum. This approach was successfully used for both expressions with measured data from three different ISEs; namely, calcium, chloride and lead ISEs.     

Genetic algorithms for protein conformation sampling and optimization in a discrete backbone dihedral angle space

We have investigated protein conformation sampling and optimization based on the genetic algorithm and discrete main chain dihedral state model. An efficient approach combining the genetic algorithm with local minimization and with a niche technique based on the sharing function is proposed. Using two different types of potential energy functions, a Go-type potential function and a knowledge-based pairwise potential energy function, and a test set containing small proteins of varying sizes and secondary structure compositions, we demonstrated the importance of local minimization and population diversity in protein conformation optimization with genetic algorithms. Some general properties of the sampled conformations such as their native-likeness and the influences of including side-chains are discussed.     

Global energy minimization of alanine dipeptide via barrier function methods

This paper presents an interior point method to determine the minimum energy conformation of alanine dipeptide. The CHARMM energy function is minimized over the internal coordinates of the atoms involved. A barrier function algorithm to determine the minimum energy conformation of peptides is proposed. Lennard-Jones 6-12 potential which is used to model the van der Waals interactions in the CHARMM energy equation is used as the barrier function for this algorithm. The results of applying the algorithm for the alanine dipeptide structure as a function of varying number of dihedral angles are reported, and they are compared with that obtained from genetic algorithm approach. In addition, the results for polyalanine structures are also reported.     

Finding reaction paths using the potential energy as reaction coordinate

The intrinsic reaction coordinate curve (IRC), normally proposed as a representation of a reaction path, is parametrized as a function of the potential energy rather than the arc-length. This change in the parametrization of the curve implies that the values of the energy of the potential energy surface points, where the IRC curve is located, play the role of reaction coordinate. We use Caratheodory's relation to derive in a rigorous manner the proposed parametrization of the IRC path. Since this Caratheodory's relation is the basis of the theory of calculus of variations, then this fact permits to reformulate the IRC model from this mathematical theory. In this mathematical theory, the character of the variational solution (either maximum or minimum) is given through the Weierstrass E-function. As proposed by Crehuet and Bofill [J. Chem. Phys. 122, 234105 (2005)], we use the minimization of the Weierstrass E-function, as a function of the potential energy, to locate an IRC path between two minima from an arbitrary curve on the potential energy surface, and then join these two minima. We also prove, from the analysis of the Weierstrass E-function, the mathematical bases for the algorithms proposed to locate the IRC path. The proposed algorithm is applied to a set of examples. Finally, the algorithm is used to locate a discontinuous, or broken, IRC path, namely, when the path connects two first order saddle points through a valley-ridged inflection point.     

定量结构-活性/性质相关性研究中变量选择方法的研究——遗传算法和几种传统算法的比较

鉴于变量选择在 QSAR/QSPR研究中的重要性 ,比较了遗传算法和几种传统的方法 ,如前进法、后退法及逐步回归法 .结果表明 ,对于研究中所用数据 ,遗传算法较几种传统的方法为好 ,其原因可能由于传统的方法陷入了局部最优 .遗传算法在变量较多的情况下方可显示出效率高和得到较好结果的优越性 .对于变量的选择 ,遗传算法是一值得推荐的有效的方法     

Optimization strategy based on genetic algorithms and neural networks applied to a polymerization process

The article presents a simple and general methodology, especially destined to the optimization of complex, strongly nonlinear systems, for which no extensive knowledge or precise models are available. The optimization problem is solved by means of a simple genetic algorithm, and the results are interpreted both from the mathematical point of view (the minimization of the objective function) and technological (the estimation of the achievement of individual objectives in multiobjective optimization). The use of a scalar objective function is supported by the fact that the genetic algorithm also computes the weights attached to the individual objectives along with the optimal values of the decision variables. The optimization strategy is accomplished in three stages: (1) the design and training of the neural model by a new method based on a genetic algorithm where information about the network is coded into the chromosomes; (2) the actual optimization based on genetic algorithms, which implies testing different values for parameters and different variants of the algorithm, computing the weights of the individual objectives and determining the optimal values for the decision variables; (3) the user's decision, who chooses a solution based on technological criteria. © 2007 Wiley Periodicals, Inc. Int J Quantum Chem, 2008     

Computational and experimental approaches for investigating membranes diffusion behavior in model diesel fuel

Genetic algorithms trained support vector regression predicting model is conducted to research diffusion behavior of methylnaphthalene and dibenzothiophene in four different membranes of polymethyl methacrylate, polymethyl acrylate, polyvinyl chloride and polyvinyl alcohol in model diesel fuel. It is found that the polyvinyl chloride is optimal membrane material for improving the diffusion selectivity of methylnaphthalene and dibenzothiophene, which demonstrates that the polyvinyl chloride membrane is favorable to the diesel fuel desulfurization. Also, molecular dynamic simulation is applied to validating the performance of genetic algorithm trained support vector regression model. The results of genetic algorithm trained support vector regression model reveal that the simulation values are well agreed with the experimental data and molecular dynamic simulation results. Meanwhile, the performance of the genetic algorithms trained support vector regression predict model is better than that of the genetic algorithms trained neural network model, which indicates that genetic algorithms trained support vector regression method offers a new prospected decision-theoretic approach to the diesel desulfurization.