Detecting purely epistatic multi-locus interactions by an omnibus permutation test on ensembles of two-locus analyses

<p>Abstract</p> <p>Background</p> <p>Purely epistatic multi-locus interactions cannot generally be detected via single-locus analysis in case-control studies of complex diseases. Recently, many two-locus and multi-locus analysis techniques have been shown to be promising for the epistasis detection. However, exhaustive multi-locus analysis requires prohibitively large computational efforts when problems involve large-scale or genome-wide data. Furthermore, there is no explicit proof that a combination of multiple two-locus analyses can lead to the correct identification of multi-locus interactions.</p> <p>Results</p> <p>The proposed 2LOmb algorithm performs an omnibus permutation test on ensembles of two-locus analyses. The algorithm consists of four main steps: two-locus analysis, a permutation test, global <it>p</it>-value determination and a progressive search for the best ensemble. 2LOmb is benchmarked against an exhaustive two-locus analysis technique, a set association approach, a correlation-based feature selection (CFS) technique and a tuned ReliefF (TuRF) technique. The simulation results indicate that 2LOmb produces a low false-positive error. Moreover, 2LOmb has the best performance in terms of an ability to identify all causative single nucleotide polymorphisms (SNPs) and a low number of output SNPs in purely epistatic two-, three- and four-locus interaction problems. The interaction models constructed from the 2LOmb outputs via a multifactor dimensionality reduction (MDR) method are also included for the confirmation of epistasis detection. 2LOmb is subsequently applied to a type 2 diabetes mellitus (T2D) data set, which is obtained as a part of the UK genome-wide genetic epidemiology study by the Wellcome Trust Case Control Consortium (WTCCC). After primarily screening for SNPs that locate within or near 372 candidate genes and exhibit no marginal single-locus effects, the T2D data set is reduced to 7,065 SNPs from 370 genes. The 2LOmb search in the reduced T2D data reveals that four intronic SNPs in <it>PGM1 </it>(phosphoglucomutase 1), two intronic SNPs in <it>LMX1A </it>(LIM homeobox transcription factor 1, alpha), two intronic SNPs in <it>PARK2 </it>(Parkinson disease (autosomal recessive, juvenile) 2, parkin) and three intronic SNPs in <it>GYS2 </it>(glycogen synthase 2 (liver)) are associated with the disease. The 2LOmb result suggests that there is no interaction between each pair of the identified genes that can be described by purely epistatic two-locus interaction models. Moreover, there are no interactions between these four genes that can be described by purely epistatic multi-locus interaction models with marginal two-locus effects. The findings provide an alternative explanation for the aetiology of T2D in a UK population.</p> <p>Conclusion</p> <p>An omnibus permutation test on ensembles of two-locus analyses can detect purely epistatic multi-locus interactions with marginal two-locus effects. The study also reveals that SNPs from large-scale or genome-wide case-control data which are discarded after single-locus analysis detects no association can still be useful for genetic epidemiology studies.</p

Abbreviated Title: 3D Container Loading Using a CCGA

1 This paper presents the use of a co-operative co-evolutionary genetic algorithm (CCGA) in conjunction with a heuristic rule for solving a 3D container loading or bin packing problem. Unlike previous works which concentrate on using either a heuristic rule or an optimisation technique to find an optimal sequence of packages which must be loaded into the containers, the proposed heuristic rule is used to partition the entire loading sequence into a number of shorter sequences. Each partitioned sequence is then represented by a species member in the CCGA search. The simulation results indicate that the use of the heuristic rule and the CCGA is highly efficient in terms of the compactness of packages in comparison to the results given by a standard genetic algorithm search. In addition, this helps to confirm that the CCGA is also suitable for use in a sequence-based optimisation problem

Hybridisation of neural networks and genetic algorithms for time-optimal control

Abstract- This paper presents the use of neural networks and genetic algorithms in time-optimal control of a closed-loop robotic system. Radial-basis function networks are used in conjunction with PID controllers in an independent joint position control to reduce tracking errors. The results indicate that using neural network controllers is more effective than using trajectory preshaping scheme, reported in early literature. Subsequently, a genetic algorithm with a weighted-sum approach and a Multi-Objective Genetic Algorithm (MOGA) are used to solve a multi-objective optimisation problem related to time-optimal control. The results indicate that the MOGA is the best method in terms of the Pareto front coverage while the genetic algorithm with a weighted-sum approach is more effective in terms of finding the best individual according to the weightedsum criteria. As a result of using both neural networks and genetic algorithms in this application, an idea of a task hybridisation between neural networks and genetic algorithms for use in a control system is also effectively demonstrated.

Multi-objective Co-operative Co-evolutionary

This paper presents the integration between two types of genetic algorithm: a multi-objective genetic algorithm (MOGA) and a co-operative co-evolutionary genetic algorithm (CCGA). The resulting algorithm is referred to as a multi-objective co-operative co-evolutionary genetic algorithm or MOCCGA. The integration between the twoalgorithms is carried out in order to improve the performance of the MOGA by adding the co-operativeco-evolutionary effect to the searchmechanisms employed by the MOGA. The MOCCGA is benchmarked against the MOGA in six different test cases. The test problems cover six differentcharacteristics that can be found within multi-objective optimisation problems: convex Pareto front, non-convex Pareto front, discrete Pareto front, multi-modality, deceptivePareto front and non-uniformity in the solution distribution. The simulation results indicate that overall the MOCCGA is superior to the MOGA in terms of the variety in solutions generated and the closeness of solutions to the true Pareto-optimal solutions

Multi-objective Optimisation

This paper presents the integration between a co-operative co-evolutionary genetic algorithm (CCGA) and four evolutionary multiobjective optimisation algorithms (EMOAs): a multi-objective genetic algorithm (MOGA), a niched Pareto genetic algorithm (NPGA), a nondominated sorting genetic algorithm (NSGA) and a controlled elitist nondominated sorting genetic algorithm (CNSGA). The resulting algorithms can be referred to as co-operative co-evolutionary multi-objective optimisation algorithms or CCMOAs. The CCMOAs are benchmarked against the EMOAs in seven test problems. The first six problems cover di#erent characteristics of multi-objective optimisation problems, namely convex Pareto front, non-convex Pareto front, discrete Pareto front, multimodality, deceptive Pareto front and non-uniformity of solution distribution

TIME-OPTIMAL PATH PLANNING AND CONTROL USING NEURAL NETWORKS AND A GENETIC ALGORITHM

This paper presents the use of neural networks and a genetic algorithm in time-optimal control of a closed-loop 3-dof robotic system. Extended Kohonen networks which contain an additional lattice of output neurons are used in conjunction with PID controllers in position control to minimise command tracking errors. The extended Kohonen networks are trained using reinforcement learning where the overall learning algorithm is derived from a selforganising feature-mapping algorithm and a delta learning rule. The results indicate that the extended Kohonen network controller is more efficient than other techniques reported in early literature when the robot is operated under normal conditions. Subsequently, a multi-objective genetic algorithm (MOGA) is used to solve an optimisation problem related to time-optimal control. This problem involves the selection of actuator torque limits and an end-effector path subject to time-optimality and tracking error constraints. Two chromosome coding schemes are explored in the investigation: Gray and integer-based coding schemes. The results suggest that the integer-based chromosome is more suitable at representing the decision variables. As a result of using both neural networks and a genetic algorithm in this application, an idea of a hybridisation between a neural network and a genetic algorithm at the task level for use in a control system is also effectively demonstrated