Graeme E. Pound

The ecological cost of sex

Why sex prevails in nature remains one of the great puzzles of evolution. Sexual reproduction has an immediate cost relative to asexual reproduction, as males only express their contribution to population growth through females. With no males to sustain, an asexual mutant can double its relative representation in the population in successive generations. This is the widely accepted ‘twofold cost of males’. Many studies have attempted to explain how sex can recoup this cost from fitness benefits associated with the recombination of parental genotypes, but these require complex biological environments that cycle over evolutionary timescales. In contrast, we have considered the ecological dynamics that govern asexual invasion. Here we show the existence of a threshold growth rate for the sexual population, above which the invasion is halted by intraspecific competition. The asexual population then exerts a weaker inhibitory effect on the carrying capacity of the sexual population than on its own carrying capacity. The stable outcome of this is coexistence on a depleted resource base. Under these ecological circumstances, longer-term benefits of sex may eventually drive out the asexual competitor.

Implementation and utilisation of a Grid-enabled problem solving environment in Matlab

In many areas of design search and optimisation one needs to utilise computational fluid dynamics (CFD) methods in order to obtain a numerical solution of the flow field in and/or around a proposed design. From this solution measures of quality for the design may be calculated, which are then used by the optimisation methods. In large models the processing time for the CFD computations can very well be many orders of magnitude larger than for the optimisation methods themselves; and the overall optimisation process usually demands a combination of computational and database resources; therefore this class of problems is well suited to Grid computing. The Geodise toolkit is a suite of tools for Grid-enabled parametric geometry generation, meshing, CFD analysis, design optimisation and search, databasing, Grid computing, and notification within the Matlab environment. These Grid services are presented to the design engineer as Matlab functions that conform to the usual syntax of Matlab. The use of the Geodise toolkit in Matlab introduces a flexible and Grid-enabled problem solving environment (PSE) for design search and optimisation. This PSE is illustrated here with two exemplar problems.

The accumulation of deleterious mutations within the frozen niche variation hypothesis

The frozen niche variation hypothesis proposes that asexual clones exploit a fraction of a total resource niche available to the sexual population from which they arise. Differences in niche breadth may allow a period of coexistence between a sexual population and the faster reproducing asexual clones. Here, we model the longer term threat to the persistence of the sexual population from an accumulation of clonal diversity, balanced by the cost to the asexual population resulting from a faster rate of accumulation of deleterious mutations. We use Monte‐Carlo simulations to quantify the interaction of niche breadth with accumulating deleterious mutations. These two mechanisms may act synergistically to prevent the extinction of the sexual population, given: (1) sufficient genetic variation, and consequently niche breadth, in the sexual population; (2) a relatively slow rate of accumulation of genetic diversity in the clonal population; (3) synergistic epistasis in the accumulation of deleterious mutations.

A grid-enabled problem solving environment (PSE) for design optimisation within Matlab

The process of design search and optimisation using computational fluid dynamics (CFD) is computationally and data intensive, a problem well-suited to Grid computing. The Geodise toolkit is a suite of Grid-enabled design optimisation and search tools within the Matlab environment. The use of these tools by the engineer is facilitated by intelligent design advisers targeted initially at CFD. The role of remote computation and data access in constructing a Grid-enabled problem solving environment is discussed. The use of the Geodise toolkit for design optimisation from within the Matlab environment is considered with an exemplar problem.

Implementation of a Grid computation toolkit for design optimisation with Matlab and Condor

The process of design search and optimisation is characterised by its computationally intensive operations, which produce a problem well suited to Grid computing. Here we present a Grid enabled computation toolkit that provides transparent and stable access to Grid compute resources from Matlab, which offers comprehensive support for the design optimisation processes. In particular, the access and integration of the Condor resource management system has been achieved by using the toolkit components that are enabled by Web service and service enhancement technologies. The use of the computation toolkit for a four-dimensional CFD parameter study with Matlab and Condor is considered as an exemplar problem.

Implementation of a grid-enabled problem solving environment in matlab

In many areas of design search and optimisation one needs to utilize Computational Fluid Dynamics (CFD) methods in order to obtain numerical solution of the flow field in and/or around a proposed design. From this solution measures of quality for the design may be calculated, which are required by optimisation methods. In large models the processing time for the CFD computatioas can very well be many orders of magnitude larger than the optimisation methods; and the overall optimisation process usually demands a combination of computational and database resources therefore this class of problems is well suited to Grid computing. The Geodise toolkit is a suite of tools for Grid-enabled parametric geometry generation, meshing, CFD analysis, design optimization and search, database, and notification tools within the Matlab environment. These grid services are presented to the design engineer as Matlab functions that conform to the usual syntax of Matlab. The use of the Geodise toolkit in Matlab introduces a flexible and Grid-enabled problem solving environment (PSE) for design search and optimisation. This PSE is illustrated here with an exemplar problem.

Towards the Semantic Grid: Putting Knowledge to Work in Design Optimisation

Modern computational Problem Solving Environments (PSEs) become more and more complex and knowledge intensive in terms of their integrated toolsets, in particular for engineering design search and optimization. Whether these toolsets can be assembled effectively to produce satisfactory results depends heavily on using the best domain practice and following decisions made by skilled engineers in practical situations. In this paper, a knowledge based approach is used to acquire this knowledge from existing sources and model it in a maintainable fashion. Ontologies are used to develop the conceptualization of a knowledge base. In order to reuse this knowledge to provide guidance at knowledge intensive points, we propose a knowledge based advisor, which can give a context-aware critique to guide users through effective operations of building domain workflows. The concept of a state panel is proposed to collect system state information, which is then reasoned about together with various task models in the JESS (Java Expert System Shell) environment. Two reasoning strategies are designed for different advising styles. A multilayer and client-server style architecture is proposed to illustrate how this advisor can be deployed to make available its knowledge advising service to a real workflow construction PSE in a maintainable fashion. Throughout we use the example of these knowledge services in the context of design optimization in engineering.

Optimization of integrated Earth System Model components using Grid-enabled data management and computation

In this paper, we present the Grid enabled data management system that has been deployed for the Grid ENabled Integrated Earth system model (GENIE) project. The database system is an augmented version of the Geodise Database Toolbox and provides a repository for scripts, binaries and output data in the GENIE framework. By exploiting the functionality available in the Geodise toolboxes we demonstrate how the database can be employed to tune parameters of coupled GENIE Earth System Model components to improve their match with observational data. A Matlab client provides a common environment for the project Virtual Organization and allows the scripting of bespoke tuning studies that can exploit multiple heterogeneous computational resources. We present the results of a number of tuning exercises performed on GENIE model components using multi‐dimensional optimization methods. In particular, we find that it is possible to successfully tune models with up to 30 free parameters using Kriging and Genetic Algorithm methods. Copyright

Multiobjective Tuning of Grid-Enabled Earth System Models Using a Non-dominated Sorting Genetic Algorithm (NSGA-II)

The tuning of parameters in climate models is essential to provide reliable long-term forecasts of Earth system behaviour. In this paper we present the first application of the multiobjective non-dominated sorting genetic algorithm (NSGA-II) to the GENIE-1 Earth System Model (ESM). Twelve model parameters are tuned to improve four objective measures of fitness to observational data. Grid computing and data handling technology is harnessed to perform the concurrent simulations that comprise the generations of the genetic algorithm. Recent advances in the method exploit Response Surface Modelling to provide surrogate models of each objective. This enables more extensive and efficient searching of the design space. We assess the performance of the NSGA-II using surrogates by repeating a tuning exercise that has been performed using a proximal analytical centre plane cutting method and the Ensemble Kalman Filter on the GENIE-1 model. We find that the multiobjective algorithm locates Pareto-optimal solutions which are of comparable quality to those obtained using the single objective optimisation methods.

Two Dimensional Airfoil Optimisation Using CFD in a Grid Computing Environment

In this paper, a two-dimensional airfoil shape optimisation problem is investigated using CFD within a grid computing environment (GCE) implemented in Matlab. The feature-based parametric CAD tool ProEngineer is used for geometry modelling. The industrial level mesh generation tool Gambit and flow solver Fluent are employed as remote services using the Globus Toolkit as the low level API. The objective of the optimisation problem is to minimize the drag-to-lift coefficient ratio for the given operating condition. A Matlab interface to the design exploration system (OPTIONS) is used to obtain solutions for the problem. The adoption of grid technologies not only simplifies the integration of proprietary software, but also makes it possible to harness distributed computational power in a consistent and flexible manner.