Response surface methodology (RSM) as a tool for optimization in analytical chemistry

This paper proposes an algorithm for optimization inspired by the imperialistic competition. Like other evolutionary ones, the proposed algorithm starts with an initial population. Population individuals called country are in two types: colonies and imperialists that all together form some empires. Imperialistic competition among these empires forms the basis of the proposed evolutionary algorithm. During this competition, weak empires collapse and powerful ones take possession of their colonies. Imperialistic competition hopefully converges to a state in which there exist only one empire and its colonies are in the same position and have the same cost as the imperialist. Applying the proposed algorithm to some of benchmark cost functions, shows its ability in dealing with different types of optimization problems.

Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition

I What Are the Ages of My Three Sons?.- 1 Why Are Some Problems Difficult to Solve?.- II How Important Is a Model?.- 2 Basic Concepts.- III What Are the Prices in 7-11?.- 3 Traditional Methods - Part 1.- IV What Are the Numbers?.- 4 Traditional Methods - Part 2.- V What's the Color of the Bear?.- 5 Escaping Local Optima.- VI How Good Is Your Intuition?.- 6 An Evolutionary Approach.- VII One of These Things Is Not Like the Others.- 7 Designing Evolutionary Algorithms.- VIII What Is the Shortest Way?.- 8 The Traveling Salesman Problem.- IX Who Owns the Zebra?.- 9 Constraint-Handling Techniques.- X Can You Tune to the Problem?.- 10 Tuning the Algorithm to the Problem.- XI Can You Mate in Two Moves?.- 11 Time-Varying Environments and Noise.- XII Day of the Week of January 1st.- 12 Neural Networks.- XIII What Was the Length of the Rope?.- 13 Fuzzy Systems.- XIV Everything Depends on Something Else.- 14 Coevolutionary Systems.- XV Who's Taller?.- 15 Multicriteria Decision-Making.- XVI Do You Like Simple Solutions?.- 16 Hybrid Systems.- 17 Summary.- Appendix A: Probability and Statistics.- A.1 Basic concepts of probability.- A.2 Random variables.- A.2.1 Discrete random variables.- A.2.2 Continuous random variables.- A.3 Descriptive statistics of random variables.- A.4 Limit theorems and inequalities.- A.5 Adding random variables.- A.6 Generating random numbers on a computer.- A.7 Estimation.- A.8 Statistical hypothesis testing.- A.9 Linear regression.- A.10 Summary.- Appendix B: Problems and Projects.- B.1 Trying some practical problems.- B.2 Reporting computational experiments with heuristic methods.- References.

/pdf/how-to-solve-it-modern-heuristics-1pu40hrf57.pdf

How to Solve It: Modern Heuristics

We have developed an efficient and reliable methodology for crystal structure prediction, merging ab initio total-energy calculations and a specifically devised evolutionary algorithm. This method allows one to predict the most stable crystal structure and a number of low-energy metastable structures for a given compound at any P-T conditions without requiring any experimental input. Extremely high (nearly 100%) success rate has been observed in a few tens of tests done so far, including ionic, covalent, metallic, and molecular structures with up to 40 atoms in the unit cell. We have been able to resolve some important problems in high-pressure crystallography and report a number of new high-pressure crystal structures (stable phases: epsilon-oxygen, new phase of sulphur, new metastable phases of carbon, sulphur and nitrogen, stable and metastable phases of CaCO3). Physical reasons for the success of this methodology are discussed.

Crystal structure prediction using ab initio evolutionary techniques: principles and applications.

https://web.iitd.ac.in/~arunku/files/CEL311_Y13/Adsorption%20Theory%20to%20practice_Dabrowski.pdf

Adsorption — from theory to practice

Here we summarize recent progress in machine learning for the chemical sciences. We outline machine-learning techniques that are suitable for addressing research questions in this domain, as well as future directions for the field. We envisage a future in which the design, synthesis, characterization and application of molecules and materials is accelerated by artificial intelligence.

/pdf/machine-learning-for-molecular-and-materials-science-2c34wzvcqw.pdf

Machine learning for molecular and materials science.

Summary: Pre-processing of chromatographic profile or mass spectral data is an important aspect of many types of proteomics and biomarker discovery experiments. Here we present a graphical computational tool, SpecAlign, that enables simultaneous visualization and manipulation of multiple datasets. SpecAlign not only provides all common processing functions, but also uniquely implements an algorithm that enables the complete alignment of each mass spectrum within a loaded dataset. We demonstrate its utility by aligning two datasets each containing six spectra; one set was acquired prior to instrument calibration and the other following calibration.

Availability:The software is free of charge and available for download from http://ptcl.chem.ox.ac.uk/~jwong/specalign. Supports Windows operating systems including Windows 9X/NT/2000/XP.

Contact: jason.wong@chem.ox.ac.uk

SpecAlign---processing and alignment of mass spectra datasets

Preprocessing of chromatographic and spectral data is an important aspect of analytical sciences. In particular, recent advances in proteomics have resulted in the generation of large data sets that require analysis. To assist accurate comparison of chemical signals, we propose two methods for the alignment of multiple spectral data sets. Based on methods previously described, each chromatograph or spectrum to be aligned is divided and aligned as individual segments to a reference. However, our methods make use of fast Fourier transform for the rapid computation of a cross-correlation function that enables alignments between samples to be optimized. The proposed methods are demonstrated in comparison with an existing method on a chromatographic and a mass spectral data set. It is shown that our methods provide an advantage of speed and a reduction of the number of input parameters required. The software implementations for the proposed alignment methods are available under the downloads section at http://ptcl.chem.ox.ac.uk/~jwong/specalign.

Application of fast Fourier transform cross-correlation for the alignment of large chromatographic and spectral datasets.

From the Publisher:
This series of short texts provides accessible accounts of a range of essential topics in chemistry. Written with the needs of the student in mind, the Oxford Chemistry Primers offer just the right level of detail for undergraduate study, and will be invaluable as a source of material commonly presented in lecture courses yet not adequately covered in existing texts. All the basic principles and facts in a particular area are presented in a clear and straightforward style, to produce concise yet comprehensive accounts of topics covered in both core and specialist courses. It is becoming evident that the techniques of artificial intelligence are useful for more than just the development of thinking machines; they constitute powerful problem-solving tools in their own right. The large-scale, complex problems most suited to AI are just those which are most difficult for conventional techniques to solve. AI methods therefore expand the range of problems in science that we can successfully tackle. Because of a recent rapid growth in computing power, these methods can now be used on a routine basis by scientists in both academic research and the commercial world. It is becoming vital that science students be exposed to and understand these techniques. This book provides an introduction to these methods, written specifically for science students. Although examples are drawn mainly from chemistry, the book is suitable for a more general audience, and should be of interest to any college-level student who wants to know more about how computers can help us to understand and interpret science.

Applications of artificial intelligence in chemistry

Artificial neural networks (ANNs) are comparatively straightforward to understand and use in the analysis of scientific data. However, this relative transparency may encourage their use in an uncritical, and therefore possibly unproductive, fashion. The geometry of a network is among the most crucial factors in the successful deployment of network tools; in this review, we cover methods that can be used to determine optimum or near-optimum geometries. These methods of determining neural network architecture include the following: (i) trial and error, in which architectures chosen semirandomly are tested and modified by the user; (ii) empirical or statistical methods, in which an ANN's internal parameters are adjusted based on the model's performance; (iii) hybrid methods, such as fuzzy inference; (iv) constructive and/or pruning algorithms, that add and/or remove neurons or weights from an initial architecture, respectively, based on a predefined link between architecture and ANN performance; (v) evolutionary strategies, which search the topology space using genetic operators to vary the neural network parameters. Several case studies illustrate the development of neural network models for applications in chemistry and chemical engineering. Copyright © 2011 John Wiley & Sons, Ltd.

Hugh M. Cartwright

Papers

Machine learning for molecular and materials science.

SpecAlign---processing and alignment of mass spectra datasets

Application of fast Fourier transform cross-correlation for the alignment of large chromatographic and spectral datasets.

Applications of artificial intelligence in chemistry

Neural networks applied in chemistry. I. Determination of the optimal topology of multilayer perceptron neural networks