Artificial neural networks as a multivariate calibration tool: modelling the Fe-Cr-Ni system in X-ray fluorescence spectroscopy

The performance of artificial neural networks (ANNs) for modeling the Cr---Ni---Fe system in quantitative x-ray fluorescence spectroscopy was compared with the classical Rasberry-Heinrich model and a previously published method applying the linear learning machine in combination with singular value decomposition. Apart from determining if ANNs were capable of modeling the desired non-linear relationships, also the effects of using non-ideal and noisy data were studied. For this goal, more than a hundred steel samples with large variations in composition were measured at their primary and secondary K¿ and Kß lines. The optimal calibration parameters for the Rasberry-Heinrich model were found from this dataset by use of a genetic algorithm. ANNs were found to be robust and to perform generally better than the other two methods in calibrating over large ranges

Evolutionary Dynamics in a Simple Model of Self-Assembly

We investigate the evolutionary dynamics of an idealised model for the robust self-assembly of two-dimensional structures called polyominoes. The model includes rules that encode interactions between sets of square tiles that drive the self-assembly process. The relationship between the model's rule set and its resulting self-assembled structure can be viewed as a genotype-phenotype map and incorporated into a genetic algorithm. The rule sets evolve under selection for specified target structures. The corresponding, complex fitness landscape generates rich evolutionary dynamics as a function of parameters such as the population size, search space size, mutation rate, and method of recombination. Furthermore, these systems are simple enough that in some cases the associated model genome space can be completely characterised, shedding light on how the evolutionary dynamics depends on the detailed structure of the fitness landscape. Finally, we apply the model to study the emergence of the preference for dihedral over cyclic symmetry observed for homomeric protein tetramers

Feature Reinforcement Learning: Part I: Unstructured MDPs

General-purpose, intelligent, learning agents cycle through sequences of observations, actions, and rewards that are complex, uncertain, unknown, and non-Markovian. On the other hand, reinforcement learning is well-developed for small finite state Markov decision processes (MDPs). Up to now, extracting the right state representations out of bare observations, that is, reducing the general agent setup to the MDP framework, is an art that involves significant effort by designers. The primary goal of this work is to automate the reduction process and thereby significantly expand the scope of many existing reinforcement learning algorithms and the agents that employ them. Before we can think of mechanizing this search for suitable MDPs, we need a formal objective criterion. The main contribution of this article is to develop such a criterion. I also integrate the various parts into one learning algorithm. Extensions to more realistic dynamic Bayesian networks are developed in Part II. The role of POMDPs is also considered there.Comment: 24 LaTeX pages, 5 diagram

Feature Markov Decision Processes

General purpose intelligent learning agents cycle through (complex,non-MDP) sequences of observations, actions, and rewards. On the other hand, reinforcement learning is well-developed for small finite state Markov Decision Processes (MDPs). So far it is an art performed by human designers to extract the right state representation out of the bare observations, i.e. to reduce the agent setup to the MDP framework. Before we can think of mechanizing this search for suitable MDPs, we need a formal objective criterion. The main contribution of this article is to develop such a criterion. I also integrate the various parts into one learning algorithm. Extensions to more realistic dynamic Bayesian networks are developed in a companion article.Comment: 7 page

Prediction of Protein Tertiary Structure using Genetic Algorithm

Proteins are essential for the biological processes in the human body. They can only perform their functions when they fold into their tertiary structure .Protein structure can be determined experimentally and computationally. Experimental methods are time consuming and high-priced and it is not always feasible to identify the protein structure experimentally. In order to predict the protein structure using computational methods, the problem is formulated as an optimization problem and the goal is to find the lowest free energy conformation. In this paper, Genetic Algorithm (GA) based optimization is used. This algorithm is adapted to search the protein conformational search space to find the lowest free energy conformation. Interestingly, the algorithm was able to find the lowest free energy conformation for a test protein (i.e. Met enkephalin) using ECEPP force fields

Postglacial colonization and parallel evolution of metal tolerance in the polyploid Cerastium alpinum

The Fennoscandian flora is characterized by a high frequency of polyploids, probably because they were more successful than diploid plants in colonizing after the last Ice Age. The first postglacial colonizers were likely poor competitors and became displaced from the lowlands as forests advanced. Consequently, many of these pioneers are currently found only above tree line. However, some have persisted within the forests on open habitats such as naturally toxic serpentine soils where succession is arrested at the pioneer stage. These populations represent relicts of former widely distributed plants. The polyploid Cerastium alpinum L. (Caryophyllaceae) grows on serpentine soils throughout Fennoscandia. C. alpinum populations on different soil types provide a model system for the study of the early postglacial colonization history of Fennoscandia. Genetic markers showed that C. alpinum populations in western Fennoscandia differ genetically from eastern populations, suggesting a two-way colonization. The two lineages meet in a hybrid zone in Northern Scandinavia where a high degree of genetic variation was found. Plants from Fennoscandia and the Western Arctic (Canada, Greenland and Iceland) shared many AFLP fragments, which suggests they originate from common refugia. The Fennoscandian populations were more distantly related to the populations in potential refugia in southern Europe. In fact, the northern populations contained AFLP fragments not found in populations in the Pyrenees and the Alps. Lack of chloroplast DNA variation indicates fast postglacial range expansions and/or a recent origin of C. alpinum. Crosses were made to establish the inheritance of enzyme markers. The results strengthen the evidence for an allopolyploid origin of C. alpinum. Adjacent serpentine and non-serpentine populations of C. alpinum provide a model system of natural replicates to test whether adaptation to serpentine is constitutive (common for all populations) or locally evolved. A growth experiment with high concentrations of nickel and magnesium, two metals that limit the fertility of serpentine soils, showed that the degree of metal tolerance reflects site-specific soil conditions. Since local adaptation was found in both the eastern and the western immigration lineages, the postglacial colonization of Fennoscandia has involved parallel evolution of metal tolerance in C. alpinum