Online Genetic Algorithms

This paper present a technique based on genetic algorithms for generating online adaptive services. Online adaptive systems provide flexible services to a mass of clients/users for maximising some system goals, they dynamically adapt the form and the content of the issued services while the population of clients evolve over time. The idea of online genetic algorithms (online GAs) is to use the online clients response behaviour as a fitness function in order to produce the next generation of services. The principle implemented in online GAs, “the application environment is the fitness”, allow modelling highly evolutionary domains where both services providers and clients change and evolve over time. The flexibility and the adaptive behaviour of this approach seems to be very relevant and promising for applications characterised by highly dynamical features such as in the web domain (online newspapers, e- markets, websites and advertising engines). Nevertheless the proposed technique has a more general aim for application environments characterised by a massive number of anonymous clients/users which require personalised services, such as in the case of many new IT applications.

XML Presentation of Documents Used for Data Exchange in Management of Genetic Resources

In the global strategy for preservation genetic resources of farm animals the implementation of information technology is of great importance. In this regards platform independent information tools and approaches for data exchange are needed in order to obtain aggregate values for regions and countries of spreading a separate breed. The current paper presents a XML based solution for data exchange in management genetic resources of farm animals’ small populations. There are specific requirements to the exchanged documents that come from the goal of data analysis. Three main types of documents are distinguished and their XML formats are discussed. DTD and XML Schema for each type are suggested. Some examples of XML documents are given also.

Offline Handwriting Recognition Using Genetic Algorithm

In this paper, a new method for offline handwriting recognition is presented. A robust algorithm for handwriting segmentation has been described here with the help of which individual characters can be segmented from a word selected from a paragraph of handwritten text image which is given as input to the module. Then each of the segmented characters are converted into column vectors of 625 values that are later fed into the advanced neural network setup that has been designed in the form of text files. The networks has been designed with quadruple layered neural network with 625 input and 26 output neurons each corresponding to a character from a-z, the outputs of all the four networks is fed into the genetic algorithm which has been developed using the concepts of correlation, with the help of this the overall network is optimized with the help of genetic algorithm thus providing us with recognized outputs with great efficiency of 71%.

A DNA Codification for Genetic Algorithms Simulation

In this paper we propose a model of encoding data into DNA strands so that this data can be used in the simulation of a genetic algorithm based on molecular operations. DNA computing is an impressive computational model that needs algorithms to work properly and efficiently. The first problem when trying to apply an algorithm in DNA computing must be how to codify the data that the algorithm will use. In a genetic algorithm the first objective must be to codify the genes, which are the main data. A concrete encoding of the genes in a single DNA strand is presented and we discuss what this codification is suitable for. Previous work on DNA coding defined bond-free languages which several properties assuring the stability of any DNA word of such a language. We prove that a bond-free language is necessary but not sufficient to codify a gene giving the correct codification.

Distributed Genetic Algorithm Implementation by Means of Remote Methods Invocation Technique – Java RMI

The aim of this work is distributed genetic algorithm implementation (so called island algorithm) to accelerate the optimum searching process in space of solutions. Distributed genetic algorithm has also smaller chances to fall in local optimum. This conception depends on mutual cooperation of the clients which realize separate working of genetic algorithms on local machines. As a tool for implementation of distributed genetic algorithm, created to produce net's applications Java technology was chosen. In Java technology, there is a technique of remote methods invocation - Java RMI. By means of invoking remote methods it can send objects between clients and server RMI.

An Adaptive Genetic Algorithm with Dynamic Population Size for Optimizing Join Queries

The problem of finding the optimal join ordering executing a query to a relational database management system is a combinatorial optimization problem, which makes deterministic exhaustive solution search unacceptable for queries with a great number of joined relations. In this work an adaptive genetic algorithm with dynamic population size is proposed for optimizing large join queries. The performance of the algorithm is compared with that of several classical non-deterministic optimization algorithms. Experiments have been performed optimizing several random queries against a randomly generated data dictionary. The proposed adaptive genetic algorithm with probabilistic selection operator outperforms in a number of test runs the canonical genetic algorithm with Elitist selection as well as two common random search strategies and proves to be a viable alternative to existing non-deterministic optimization approaches.

Implementation of Genetic Algorithms for Transit Points Arrangement

The problem of transit points arrangement is presented in the paper. This issue is connected with accuracy of tariff distance calculation and it is the urgent problem at present. Was showed that standard method of tariff distance discovering is not optimal. The Genetic Algorithms are used in optimization problem resolution. The UML application class diagram and class content are showed. In the end the example of transit points arrangement is represented.

Self Evolving Character Recognition Using Genetic Operators

In this paper, a novel approach for character recognition has been presented with the help of genetic operators which have evolved from biological genetics and help us to achieve highly accurate results. A genetic algorithm approach has been described in which the biological haploid chromosomes have been implemented using a single row bit pattern of 315 values which have been operated upon by various genetic operators. A set of characters are taken as an initial population from which various new generations of characters are generated with the help of selection, crossover and mutation. Variations of population of characters are evolved from which the fittest solution is found by subjecting the various populations to a new fitness function developed. The methodology works and reduces the dissimilarity coefficient found by the fitness function between the character to be recognized and members of the populations and on reaching threshold limit of the error found from dissimilarity, it recognizes the character. As the new population is being generated from the older population, traits are passed on from one generation to another. We present a methodology with the help of which we are able to achieve highly efficient character recognition.

The genetic and environmental effects of parental age on the expression of psychopathology in adoptees

Secondary analysis of 581 adoptees was utilized to determine if parental age is related, either genetically or environmentally, to the development of psychopathology. The significant results showed that proband adoptees (with psychopathology in biologic relatives) with younger birthparents had increased alcohol abuse and those with younger birthfathers had increased antisocial personality while adoptees with older birthparents had increased depression. Analyses on control adoptees (with background free of known genetic disturbances) showed that those with younger adoptive mothers had increased antisocial personality and drug abuse and those with younger adoptive fathers had increased antisocial personality while adoptees with older adoptive fathers had increased depression. Implications of these findings are that adoptees with both younger birth and adoptive parents are more likely to have externalizing symptoms, while adoptees with both older birth and adoptive parents are more like to have internalizing symptoms. This information is beneficial to those involved in adoption placement.

Model updating using genetic algorithms with sequential niche technique

The authors would like to express their gratitude to organizations and people that supported this research. Piotr Omenzetter’s work within the Lloyd’s Register Foundation Centre for Safety and Reliability Engineering at the University of Aberdeen is supported by Lloyd’s Register Foundation. The Foundation helps to protect life and property by supporting engineering-related education, public engagement and the application of research. Ben Ryder of Aurecon and Graeme Cummings of HEB Construction assisted in obtaining access to the bridge and information for modelling. Luke Williams and Graham Bougen, undergraduate research students, assisted with testing.

Genetic Analysis of Mitotic Recombination in Saccharomyces cerevisiae

Mitotic genome instability can occur during the repair of double-strand breaks (DSBs) in DNA, which arise from endogenous and exogenous sources. Studying the mechanisms of DNA repair in the budding yeast, Saccharomyces cerevisiae has shown that Homologous Recombination (HR) is a vital repair mechanism for DSBs. HR can result in a crossover event, in which the broken molecule reciprocally exchanges information with a homologous repair template. The current model of double-strand break repair (DSBR) also allows for a tract of information to non-reciprocally transfer from the template molecule to the broken molecule. These “gene conversion” events can vary in size and can occur in conjunction with a crossover event or in isolation. The frequency and size of gene conversions in isolation and gene conversions associated with crossing over has been a source of debate due to the variation in systems used to detect gene conversions and the context in which the gene conversions are measured.

In Chapter 2, I use an unbiased system that measures the frequency and size of gene conversion events, as well as the association of gene conversion events with crossing over between homologs in diploid yeast. We show mitotic gene conversions occur at a rate of 1.3x10-6 per cell division, are either large (median 54.0kb) or small (median 6.4kb), and are associated with crossing over 43% of the time.

DSBs can arise from endogenous cellular processes such as replication and transcription. Two important RNA/DNA hybrids are involved in replication and transcription: R-loops, which form when an RNA transcript base pairs with the DNA template and displaces the non-template DNA strand, and ribonucleotides embedded into DNA (rNMPs), which arise when replicative polymerase errors insert ribonucleotide instead of deoxyribonucleotide triphosphates. RNaseH1 (encoded by RNH1) and RNaseH2 (whose catalytic subunit is encoded by RNH201) both recognize and degrade the RNA in within R-loops while RNaseH2 alone recognizes, nicks, and initiates removal of rNMPs embedded into DNA. Due to their redundant abilities to act on RNA:DNA hybrids, aberrant removal of rNMPs from DNA has been thought to lead to genome instability in an rnh201Δ background.

In Chapter 3, I characterize (1) non-selective genome-wide homologous recombination events and (2) crossing over on chromosome IV in mutants defective in RNaseH1, RNaseH2, or RNaseH1 and RNaseH2. Using a mutant DNA polymerase that incorporates 4-fold fewer rNMPs than wild type, I demonstrate that the primary recombinogenic lesion in the RNaseH2-defective genome is not rNMPs, but rather R-loops. This work suggests different in-vivo roles for RNaseH1 and RNaseH2 in resolving R-loops in yeast and is consistent with R-loops, not rNMPs, being the the likely source of pathology in Aicardi-Goutières Syndrome patients defective in RNaseH2.

Sampling information, length measurements, DNA alignments, models and phylogenetics trees for Oithona similis s.l.

Traditionally, many small-sized copepod species are considered to be widespread, bipolar or cosmopolitan. However, these large-scale distribution patterns need to be re-examined in view of increasing evidence of cryptic and pseudo-cryptic speciation in pelagic copepods. Here, we present a phylogeographic study of Oithona similis s.l. populations from the Arctic Ocean, the Southern Ocean and its northern boundaries, the North Atlantic and the Mediterrranean Sea. O. similis s.l. is considered as one of the most abundant species in temperate to polar oceans and acts as an important link in the trophic network between the microbial loop and higher trophic levels such as fish larvae. Two gene fragments were analysed: the mitochondrial cytochrome oxidase c subunit I (COI), and the nuclear ribosomal 28S genetic marker. Seven distinct, geographically delimitated, mitochondrial lineages could be identified, with divergences among the lineages ranging from 8 to 24 %, thus representing most likely cryptic or pseudocryptic species within O. similis s.l. Four lineages were identified within or close to the borders of the Southern Ocean, one lineage in the Arctic Ocean and two lineages in the temperate Northern hemisphere. Surprisingly the Arctic lineage was more closely related to lineages from the Southern hemisphere than to the other lineages from the Northern hemisphere, suggesting that geographic proximity is a rather poor predictor of how closely related the clades are on a genetic level. Molecular clock application revealed that the evolutionary history of O. similis s.l. is possibly closely associated with the reorganization of the ocean circulation in the mid Miocene and may be an example of allopatric speciation in the pelagic zone.

Fisheries stocks from an ecological perspective: Disentangling ecological connectivity from genetic interchange

The concept of a stock of fish as a management unit has been around for well over a hundred years, and this has formed the basis for fisheries science. Methods for delimiting stocks have advanced considerably over recent years, including genetic, telemetric, tagging, geochemical and phenotypic information. In parallel with these developments, concepts in population ecology such as meta-population dynamics and connectivity have advanced. The pragmatic view of stocks has always accepted some mixing during spawning, feeding and/or larval drift. Here we consider the mismatch between ecological connectivity of a matrix of populations typically focussed on demographic measurements, and genetic connectivity of populations that focus on genetic exchange detected using modern molecular approaches. We suggest that from an ecological-connectivity perspective populations can be delimited as management units if there is limited exchange during recruitment or via migration in most years. From a genetic-connectivity perspective such limited exchange can maintain panmixia. We use case-studies of species endangered by overexploitation and/or habitat degradation to show how current methods of stock delimitation can help in managing populations and in conservation.

Fisheries stocks from an ecological perspective: Disentangling ecological connectivity from genetic interchange

The concept of a stock of fish as a management unit has been around for well over a hundred years, and this has formed the basis for fisheries science. Methods for delimiting stocks have advanced considerably over recent years, including genetic, telemetric, tagging, geochemical and phenotypic information. In parallel with these developments, concepts in population ecology such as meta-population dynamics and connectivity have advanced. The pragmatic view of stocks has always accepted some mixing during spawning, feeding and/or larval drift. Here we consider the mismatch between ecological connectivity of a matrix of populations typically focussed on demographic measurements, and genetic connectivity of populations that focus on genetic exchange detected using modern molecular approaches. We suggest that from an ecological-connectivity perspective populations can be delimited as management units if there is limited exchange during recruitment or via migration in most years. From a genetic-connectivity perspective such limited exchange can maintain panmixia. We use case-studies of species endangered by overexploitation and/or habitat degradation to show how current methods of stock delimitation can help in managing populations and in conservation.

Directional genetic differentiation and relative migration

Understanding the population structure and patterns of gene flow within species is of fundamental importance to the study of evolution. In the fields of population and evolutionary genetics, measures of genetic differentiation are commonly used to gather this information. One potential caveat is that these measures assume gene flow to be symmetric. However, asymmetric gene flow is common in nature, especially in systems driven by physical processes such as wind or water currents. As information about levels of asymmetric gene flow among populations is essential for the correct interpretation of the distribution of contemporary genetic diversity within species, this should not be overlooked. To obtain information on asymmetric migration patterns from genetic data, complex models based on maximum-likelihood or Bayesian approaches generally need to be employed, often at great computational cost. Here, a new simpler and more efficient approach for understanding gene flow patterns is presented. This approach allows the estimation of directional components of genetic divergence between pairs of populations at low computational effort, using any of the classical or modern measures of genetic differentiation. These directional measures of genetic differentiation can further be used to calculate directional relative migration and to detect asymmetries in gene flow patterns. This can be done in a user-friendly web application called divMigrate-online introduced in this study. Using simulated data sets with known gene flow regimes, we demonstrate that the method is capable of resolving complex migration patterns under a range of study designs.