Optimization of regenerative cycle with open feed water heater using genetic algorithms and neural networks

This article determines the operating conditions leading to maximum work in a regenerative cycle with an open feed water heater through a procedure that combines the use of artificial neural networks (ANNs) and genetic algorithms (GAs). Water is an active fluid in the thermodynamical cycle; an objective function is obtained by using vapor enthalpy (a nonlinear function of operating conditions). Utilizing classical methods for maximizing the objective function usually leads to suboptimal solutions. Therefore, this article uses ANNs to estimate the steam properties as a function of operating conditions and GAs to optimize the thermodynamical cycle. The operating conditions are chosen with the aim of gaining maximum work in a boiler for a specific heat. To estimate the thermodynamic properties, an ANN was used to provide the necessary data required in the GA calculation.     

Optimizing doped libraries by using genetic algorithms

The insertion of random sequences into protein-encoding genes in combination with biologicalselection techniques has become a valuable tool in the design of molecules that have usefuland possibly novel properties. By employing highly effective screening protocols, a functionaland unique structure that had not been anticipated can be distinguished among a hugecollection of inactive molecules that together represent all possible amino acid combinations.This technique is severely limited by its restriction to a library of manageable size. Oneapproach for limiting the size of a mutant library relies on doping schemes, where subsetsof amino acids are generated that reveal only certain combinations of amino acids in a proteinsequence. Three mononucleotide mixtures for each codon concerned must be designed, suchthat the resulting codons that are assembled during chemical gene synthesis represent thedesired amino acid mixture on the level of the translated protein. In this paper we present adoping algorithm that reverse translates a desired mixture of certain amino acids into threemixtures of mononucleotides. The algorithm is designed to optimally bias these mixturestowards the codons of choice. This approach combines a genetic algorithm with localoptimization strategies based on the downhill simplex method. Disparate relativerepresentations of all amino acids (and stop codons) within a target set can be generated.Optional weighing factors are employed to emphasize the frequencies of certain amino acidsand their codon usage, and to compensate for reaction rates of different mononucleotidebuilding blocks (synthons) during chemical DNA synthesis. The effect of statistical errors thataccompany an experimental realization of calculated nucleotide mixtures on the generatedmixtures of amino acids is simulated. These simulations show that the robustness of differentoptima with respect to small deviations from calculated values depends on their concomitantfitness. Furthermore, the calculations probe the fitness landscape locally and allow apreliminary assessment of its structure.     

Energy minimization of peptide analogues using genetic algorithms

A genetic algorithm is used to minimize the energy of peptide analogues in the dihedral angle space. It is interfaced to MOPAC, which computes the energy employing the AM1 Hamiltonian. The genetic algorithm identified the global energy minimum of glycine dipeptide analogue, alanine dipeptide analogue, diglycine, and dialanine. It identified three low-energy conformations of tetraalanine, including the reported global minimum, all of which contained three hydrogen bonds. A structure with a lower energy than the reported global minimum has been generated in which one hydrogen bond is replaced by another one. © 1995 John Wiley & Sons, Inc.     

Metabolic profiling using principal component analysis, discriminant partial least squares, and genetic algorithms

The aim of this study was to evaluate evolutionary variable selection methods in improving the classification of ¹H nuclear magnetic resonance (NMR) metabonomic profiles, and to identify the metabolites that are responsible for the classification. Human plasma, urine, and saliva from a group of 150 healthy male and female subjects were subjected to ¹H NMR-based metabonomic analysis. The ¹H NMR spectra were analyzed using two pattern recognition methods, principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA), to identify metabolites responsible for gender differences. The use of genetic algorithms (GA) for variable selection methods was found to enhance the classification performance of the PLS-DA models. The loading plots obtained by PCA and PLS-DA were compared and various metabolites were identified that are responsible for the observed separations. These results demonstrated that our approach is capable of identifying the metabolites that are important for the discrimination of classes of individuals of similar physiological conditions.     

On the suitability of different representations of solid catalysts for combinatorial library design by genetic algorithms

Genetic algorithms are widely used to solve and optimize combinatorial problems and are more often applied for library design in combinatorial chemistry. Because of their flexibility, however, their implementation can be challenging. In this study, the influence of the representation of solid catalysts on the performance of genetic algorithms was systematically investigated on the basis of a new, constrained, multiobjective, combinatorial test problem with properties common to problems in combinatorial materials science. Constraints were satisfied by penalty functions, repair algorithms, or special representations. The tests were performed using three state-of-the-art evolutionary multiobjective algorithms by performing 100 optimization runs for each algorithm and test case. Experimental data obtained during the optimization of a noble metal-free solid catalyst system active in the selective catalytic reduction of nitric oxide with propene was used to build up a predictive model to validate the results of the theoretical test problem. A significant influence of the representation on the optimization performance was observed. Binary encodings were found to be the preferred encoding in most of the cases, and depending on the experimental test unit, repair algorithms or penalty functions performed best.     

Powder pattern indexing using the weighted crosscorrelation and genetic algorithms

X-ray diffraction is a powerful technique for investigating the structure of crystals and crystalline powders. Unfortunately, for powders, the first step in the structure elucidation process, retrieving the unit cell parameters (indexing), is still very critical. In the present article, an improved approach to powder pattern indexing is presented. The proposed method matches peak positions from experimental X-ray powder patterns with peak positions from trial cells using a recently published method for pattern comparison (weighted crosscorrelation). Trial cells are optimized with Genetic Algorithms. Patterns are not pretreated to remove any existing zero point shift, as this is determined during optimization. Another improvement is the peak assignment procedure. This assignment is needed for determining the similarity between lines from trial cells and experiment. It no longer allows calculated peaks to be assigned twice to different experimental peaks, which is beneficial for the indexing process. The procedure proves to be robust with respect to false peaks and accidental or systematic absensences of reflections, and is successfully applied to powder patterns originating from orthorhombic, monoclinic, and triclinic compounds measured with synchrotron as well as with conventional laboratory X-ray diffractometers.     

Multilinear gradient elution optimisation in reversed-phase liquid chromatography using genetic algorithms

The treatment presented in a recent paper [P. Nikitas, A. Pappa-Louisi, J. Chromatogr. A, 1068 (2005) 279] is extended to multilinear gradients, i.e. continuous gradients consisting of a certain number of linear portions. Thus, the experimental lnk versus phi curve, where k is the retention factor of a sample solute under isocratic conditions and phi is the volume fraction of the organic modifier in the water-organic mobile phase, is subdivided into a finite number of linear portions resulting in simple analytical expressions for the solute gradient retention time. These expressions of the retention time are directly used in an optimisation technique based on genetic algorithms. This technique involves first the determination of the theoretical dependence of k upon phi by means of gradient measurements, which in turn is used by the genetic algorithm for the prediction of the best gradient profile. The validity of the analytical expressions and the effectiveness of the optimisation technique were tested using fifteen underivatized amino acids and related compounds with mobile phases modified by acetonitrile. It was found that the adopted methodology exhibits significant advantages and it can lead to high quality predictions of the gradient retention times and optimisation results.     

Optimization methodology based on neural networks and genetic algorithms applied to electro-coagulation processes

An optimization methodology based on neural networks and genetic algorithms was developed and used to optimize a real world process — an electro-coagulation process involving three pollutants at different concentrations: kaolin (250–1000 mg L^?1), Eriochrome Black T solutions (50–200 mg L?1), and oil/water emulsion (1500–4500 mg L^?1). Feed-forward neural networks using heterogeneous combination of transfer functions were developed, leading to good results in the validation stage (relative error about 8%). The parameters of the process (concentration of pollutant, time, pH₀, conductivity and current density) were optimized handling the genetic algorithm parameters, in order to obtain a maximum removal efficiency for each pollutant. Therefore, the optimization methodology combines neural networks as modeling tools with genetic algorithms as solving method. Validation of the optimization results using supplementary experimental data reveals errors under 11%.      

Massive docking of flexible ligands using environmental niches in parallelized genetic algorithms

Virtual screening of large libraries of small compounds requires fast and reliable automatic docking methods. In this article we present a parallel implementation of a genetic algorithm (GA) and the implementation of an enhanced genetic algorithm (EGA) with niching that lead to remarkable speedups compared to the original version AutoDock 3.0. The niching concept is introduced naturally by sharing genetic information between evolutions of subpopulations that run independently, each on one CPU. A unique set of additionally introduced search parameters that control this information flow has been obtained for drug‐like molecules based on the detailed study of three test cases of different complexity. The average docking time for one compound is of 8.6 s using eight R10,000 processors running at 200 MHz in an Origin 2000 computer. Different genetic algorithms with and without local search (LS) have been compared on an equal workload basis showing EGA/LS to be superior over all alternatives because it finds lower energy solutions faster and more often, particularly for high dimensionality problems. © 2001 John Wiley & Sons, Inc. J Comput Chem 22: 1971–1982, 2001     

Implementation of the multi-channel monolith reactor in an optimisation procedure for heterogeneous oxidation catalysts based on genetic algorithms

A multi-criteria optimisation procedure based on genetic algorithms is carried out in search of advanced heterogeneous catalysts for total oxidation. Simple but flexible software routines have been created to be applied within a search space of more then 150,000 individuals. The general catalyst design includes mono-, bi- and trimetallic compositions assembled out of 49 different metals and depleted on an Al2O3 support in up to nine amount levels. As an efficient tool for high-throughput screening and perfectly matched to the requirements of heterogeneous gas phase catalysis - especially for applications technically run in honeycomb structures - the multi-channel monolith reactor is implemented to evaluate the catalyst performances. Out of a multi-component feed-gas, the conversion rates of carbon monoxide (CO) and a model hydrocarbon (HC) are monitored in parallel. In combination with further restrictions to preparation and pre-treatment a primary screening can be conducted, promising to provide results close to technically applied catalysts. Presented are the resulting performances of the optimisation process for the first catalyst generations and the prospect of its auto-adaptation to specified optimisation goals.     

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     

Optimal design of nanoplasmonic materials using genetic algorithms as a multiparameter optimization tool

An optimal control approach based on multiple parameter genetic algorithms is applied to the design of plasmonic nanoconstructs with predetermined optical properties and functionalities. We first develop nanoscale metallic lenses that focus an incident plane wave onto a prespecified, spatially confined spot. Our results illustrate the mechanism of energy flow through wires and cavities. Next we design a periodic array of silver particles to modify the polarization of an incident, linearly polarized plane wave in a desired fashion while localizing the light in space. The results provide insight into the structural features that determine the birefringence properties of metal nanoparticles and their arrays. Of the variety of potential applications that may be envisioned, we note the design of nanoscale light sources with controllable coherence and polarization properties that could serve for coherent control of molecular, electronic, or electromechanical dynamics in the nanoscale.     

Structural optimization of model colloidal clusters at the air-water interface using genetic algorithms

The structure of colloidal clusters observed at the air-water interface is studied theoretically in this paper using a recently developed global optimization routine based on a genetic algorithm. A model potential with a secondary minimum produced from the attractive van der Waals interaction and repulsive dipolar interaction is used for the colloid-colloid interaction. The structures predicted from the genetic algorithm are not always consistent with the experimental observations; the theoretical results predict the growth of cluster as a spreading of triangular networks, however, the experimental observations seem to suggest the formation of a circular shell structure. More thorough theoretical as well as experimental studies are called for in order to obtain more conclusive evidence regarding these matters.     

Fluorenyl based syndiotactic specific metallocene catalysts structural features,origin of syndiospecificity

The stereochemistry of propylene insertion/propagation reactions with a variety of C_s symmetric fluorenyl- containing single site catalysts is discussed. Our recent results indicate that independent of the chemical composition of the ancillary ligand fragments, or nature of the transition metal, active sites with local C_s symmetry and enantiotopic coordination positions behave syndioselectively in the general context of chain migratory insertion mechanism. Perfect bilateral symmetry neither exists nor is required in these processes. In this context the mechanism of syndiospecific polymerization is revisited by taking into account the structural characteristics and catalytic behavior of the original metallocene based (η⁵-C₅H₄-CMe₂-η⁵-C₁₃H₈) MCl₂/ MAO; M = Zr ( 1 ), Hf ( 2 ) catalyst systems and new syndiotactic specific systems including (η⁵-C₅H₄-CPh2-η5-3,6-di-tBut-C₁₃H₆)ZrCl₂ ( 3 ), η¹,η⁵-(μMe₂Si)(3,6-di-tBut-Flu)(t-ButN)MCl₂/ MAO; M =Ti ( 4 ), Zr ( 5 ) and η¹,η⁵-(μMe₂Si)(2,7-di-tBut-Flu)(t-ButN)MCl₂/ MAO; M = Ti ( 6 ), Zr ( 7 ).     

Adaptive multicomponent analysis by genetic algorithms

The applicability of genetic algorithms for solving multicomponent analyses is systematically examined. As a genetic algorithm (GA), the basic proposal of Goldberg is implemented in a straightforward manner to simulate multicomponent analyses in analogy to the well-established UV-vis or IR methods, especially multicomponent regression. The main focus of the study is to investigate the behavior of the genetic algorithm in order to compare it with the well-known behavior of multicomponent regression. A remarkable difference between the two methods is that the genetic algorithm method does not need any calibration procedure because of its pure searching characteristic. As important features of multicomponent systems, the degree of signal overlap (selectivity), the behavior of systems with known and unknown component numbers and qualities, and linear as well as nonlinear relationships between the analytical signal and concentration are varied within the simulations. According to multicomponent regression, recovering concentrations by a genetic algorithm is of limited applicability with the exception of systems at a low degree of signal overlap. On the other hand, the recovery of a probe spectrum in the analytical process always gives satisfactory results independent of the features of the probe system. The genetic algorithm obviously shows autoadaptive behavior in probe spectrum recovery. The quality and quantity of the resulting components may dramatically differ from the given probe, although the resulting spectrum is nearly the same. In such cases, the resulting component mixture can be interpreted as an imitation of the probe. As well probe spectra, theoretically designed spectra can also be autoadapted by genetic algorithms. The only limitation is that the desired spectrum must, of course, be incorporated into the search space defined by the involved components. Furthermore, a spectral signal is only one single property of a chemical compound or mixture. Because of the nonlinear search characteristic of genetic algorithms, any other chemical or physical property can also be treated as a desired property. Therefore, the conclusion of the study is well-founded that an old challenge of applied chemistry, namely, the development of new chemical products with desired properties, seems to be reachable under the control of genetic algorithms.     

Application of genetic algorithms in molecular modeling

A genetic algorithm has been developed for molecular mechanics calculations. It has been proved to be a robust and efficient structure optimization technique. Because it uses randomly generated starting structures and stochastic operators, the resulting structures are not subjected to the chemist's bias. © 1994 by John Wiley & Sons, Inc.