Computing Epistasis of Template Functions Through Walsh Transforms

Template functions have been introduced as a class of test functions, allowing to study the convergence behaviour of genetic algorithms. In this note, we show how to use Walsh transforms to calculate the normalized epistasis of these functions

Risk-based reliability allocation at component level in non-repairable systems by using evolutionary algorithm

The approach for setting system reliability in the risk-based reliability allocation (RBRA) method is driven solely by the amount of ‘total losses’ (sum of reliability investment and risk of failure) associated with a non-repairable system failure. For a system consisting of many components, reliability allocation by RBRA method becomes a very complex combinatorial optimisation problem particularly if large numbers of alternatives, with different levels of reliability and associated cost, are considered for each component. Furthermore, the complexity of this problem is magnified when the relationship between cost and reliability assumed to be nonlinear and non-monotone. An optimisation algorithm (OA) is therefore developed in this research to demonstrate the solution for such difficult problems. The core design of the OA originates from the fundamental concepts of basic Evolutionary Algorithms which are well known for emulating Natural process of evolution in solving complex optimisation problems through computer simulations of the key genetic operations such as 'reproduction', ‘crossover’ and ‘mutation’. However, the OA has been designed with significantly different model of evolution (for identifying valuable parent solutions and subsequently turning them into even better child solutions) compared to the classical genetic model for ensuring rapid and efficient convergence of the search process towards an optimum solution. The vital features of this OA model are 'generation of all populations (samples) with unique chromosomes (solutions)', 'working exclusively with the elite chromosomes in each iteration' and 'application of prudently designed genetic operators on the elite chromosomes with extra emphasis on mutation operation'. For each possible combination of alternatives, both system reliability and cost of failure is computed by means of Monte-Carlo simulation technique. For validation purposes, the optimisation algorithm is first applied to solve an already published reliability optimisation problem with constraint on some target level of system reliability, which is required to be achieved at a minimum system cost. After successful validation, the viability of the OA is demonstrated by showing its application in optimising four different non-repairable sample systems in view of the risk based reliability allocation method. Each system is assumed to have discrete choice of component data set, showing monotonically increasing cost and reliability relationship among the alternatives, and a fixed amount associated with cost of failure. While this optimisation process is the main objective of the research study, two variations are also introduced in this process for the purpose of undertaking parametric studies. To study the effects of changes in the reliability investment on system reliability and total loss, the first variation involves using a different choice of discrete data set exhibiting a non-monotonically increasing relationship between cost and reliability among the alternatives. To study the effects of risk of failure, the second variation in the optimisation process is introduced by means of a different cost of failure amount, associated with a given non-repairable system failure. The optimisation processes show very interesting results between system reliability and total loss. For instance, it is observed that while maximum reliability can generally be associated with high total loss and low risk of failure, the minimum observed value of the total loss is not always associated with minimum system reliability. Therefore, the results exhibit various levels of system reliability and total loss with both values showing strong sensitivity towards the selected combination of component alternatives. The first parametric study shows that second data set (nonmonotone) creates more opportunities for the optimisation process for producing better values of the loss function since cheaper components with higher reliabilities can be selected with higher probabilities. In the second parametric study, it can be seen that the reduction in the cost of failure amount reduces the size of risk of failure which also increases the chances of using cheaper components with lower levels of reliability hence producing lower values of the loss functions. The research study concludes that the risk-based reliability allocation method together with the optimisation algorithm can be used as a powerful tool for highlighting various levels of system reliabilities with associated total losses for any given system in consideration. This notion can be further extended in selecting optimal system configuration from various competing topologies. With such information to hand, reliability engineers can streamline complicated system designs in view of the required level of system reliability with minimum associated total cost of premature failure. In all cases studied, the run time of the optimisation algorithm increases linearly with the complexity of the algorithm and due to its unique model of evolution, it appears to conduct very detailed multi-directional search across the solution space in fewer generations - a very important attribute for solving the kind of problem studied in this research. Consequently, it converges rapidly towards optimum solution unlike the classical genetic algorithm which gradually reaches the optimum, when successful. The research also identifies key areas for future development with the scope to expand in various other dimensions due to its interdisciplinary applications

The use of some non-minimal representations to improve the effectiveness of genetic algorithms

In the unitation representation used in genetic algorithms, the number of genotypes that map onto each phenotype varies greatly. This leads to an attractor in phenotype space which impairs the performance of the genetic algorithm. The attractor is illustrated theoretically and empirically. A new representation, called the length varying representation (LVR), allows unitation chromosomes of varying length (and hence with a variety of attractors) to coexist. Chromosomes whose lengths yield attractors close to optima come to dominate the population. The LVR is shown to be more effective than the unitation representation against a variety of fitness functions. However, the LVR preferentially converges towards the low end of phenotype space. The phenotype shift representation (PSR), which retains the ability of the LVR to select for attractors that are close to optima, whilst using a fixed length chromosome and thus avoiding the asymmetries inherent in the LVR, is defined. The PSR is more effective than the LVR and the results compare favourably with previously published results from eight other algorithms. The internal operation of the PSR is investigated. The PSR is extended to cover multi-dimensional problems. The premise that improvements in performance may be attained by the insertion of introns, non-coding sequences affecting linkage, into traditional bit string chromosomes is investigated. In this investigation, using a population size of 50, there was no evidence of improvement in performance. However, the position of the optima relative to the hamming cliffs is shown to have a major effect on the performance of the genetic algorithm using the binary representation, and the inadequacy of the traditional crossover and mutation operators in this context is demonstrated. Also, the disallowance of duplicate population members was found to improve performance over the standard generational replacement strategy in all trials

Survey of FPGA applications in the period 2000 – 2015 (Technical Report)

Romoth J, Porrmann M, Rückert U. Survey of FPGA applications in the period 2000 – 2015 (Technical Report).; 2017.Since their introduction, FPGAs can be seen in more and more different fields of applications. The key advantage is the combination of software-like flexibility with the performance otherwise common to hardware. Nevertheless, every application field introduces special requirements to the used computational architecture. This paper provides an overview of the different topics FPGAs have been used for in the last 15 years of research and why they have been chosen over other processing units like e.g. CPUs

Horizontal transfer, selection and maintenance of antibiotic resistance determinants

Scientific abstract Antibiotic resistance, especially in Gram-negative pathogens, represents a substantial clinical and financial burden to our society. The presented work investigated mechanisms and evolutionary dynamics that promote the emergence and maintenance of resistance in bacteria. In the first study, conserved collateral susceptibility changes were identified across resistant uropathogenic Escherichia coli, which included also changes towards increased sensitivity of these isolates to certain antibiotics. This so-called collateral sensitivity potentiated the effect of antibiotics and prevented the selection of resistant isolates compared to wildtype strains. The mechanism and fitness cost of resistance were important predictors of collateral responses in resistant bacteria, and their rapid clinical identification could inform future evolution-based infection treatment. The second study demonstrated the potential of a transposable element (Tn1) to spread antibiotic resistance during natural transformation of Acinetobacter baylyi. In the course of this horizontal gene transfer mechanism, Tn1-containing DNA entered the bacterial cell, and specific host, as well as transposon proteins, facilitated Tn1-insertion into the recipient chromosome. A mechanistic model of transposition-mediated natural transformation from a circular, cytoplasmic intermediate is presented. In the third study, uropathogenic E. coli improved its permissiveness towards two unrelated multidrug resistance plasmids while adapting to a new environmental niche. Mutations in the CCR and ArcAB regulatory systems resulted in transcriptional downregulation of plasmid genes and explained plasmid cost reduction. The presented evolutionary dynamics improve our understanding of successful associations between bacterial pathogens and resistance plasmids and provide a novel solution to the so-called ‘plasmid paradox’. In a broader perspective, the findings of this thesis advanced our knowledge on the selection, spread and maintenance of antibiotic resistance in bacteria, which is important to counteract its evolution

Affine image registration using genetic algorithms and evolutionary strategies

This thesis investigates the application of evolutionary algorithms to align two or more 2-D images by means of image registration. The proposed search strategy is a transformation parameters-based approach involving the affine transform. A noisy objective function is proposed and tested using two well-known evolutionary algorithms (EAs), the genetic algorithm (GA) as well as the evolutionary strategies (ES) that are suitable for this particular ill-posed problem. In contrast with GA, which was originally designed to work on binary representation, ES was originally developed to work in continuous search spaces. Surprisingly, results of the proposed real coded genetic algorithm are far superior when compared to results obtained from evolutionary strategies’ framework for the problem at hand. The real coded GA uses Simulated Binary Crossover (SBX), a parent-centric recombination operator that has shown to deliver a good performance in many optimization problems in the continuous domain. In addition, a new technique for matching points, between a warped and static images by using a randomized ordering when visiting the points during the matching procedure, is proposed. This new technique makes the evaluation of the objective function somewhat noisy, but GAs and other population-based search algorithms have been shown to cope well with noisy fitness evaluations. The results obtained from GA formulation are competitive to those obtained by the state-of-the-art classical methods in image registration, confirming the usefulness of the proposed noisy objective function and the suitability of SBX as a recombination operator for this type of problem