Landscape state machines: Tools for evolutionary algorithm performance analyses and landscape/algorithm mapping

Many evolutionary algorithm applications involve either fitness functions with high time complexity or large dimensionality (hence very many fitness evaluations will typically be needed) or both. In such circumstances, there is a dire need to tune various features of the algorithm well so that performance and time savings are optimized. However, these are precisely the circumstances in which prior tuning is very costly in time and resources. There is hence a need for methods which enable fast prior tuning in such cases. We describe a candidate technique for this purpose, in which we model a landscape as a finite state machine, inferred from preliminary sampling runs. In prior algorithm-tuning trials, we can replace the 'real' landscape with the model, enabling extremely fast tuning, saving far more time than was required to infer the model. Preliminary results indicate much promise, though much work needs to be done to establish various aspects of the conditions under which it can be most beneficially used. A main limitation of the method as described here is a restriction to mutation-only algorithms, but there are various ways to address this and other limitations.

Mixed initial-boundary value problem in particle modeling of microelectronic devices

The Boltzmann equation in presence of boundary and initial conditions, which describes the general case of carrier transport in microelectronic devices is analysed in terms of Monte Carlo theory. The classical Ensemble Monte Carlo algorithm which has been devised by merely phenomenological considerations of the initial and boundary carrier contributions is now derived in a formal way. The approach allows to suggest a set of event-biasing algorithms for statistical enhancement as an alternative of the population control technique, which is virtually the only algorithm currently used in particle simulators. The scheme of the self-consistent coupling of Boltzmann and Poisson equation is considered for the case of weighted particles. It is shown that particles survive the successive iteration steps.

Towards self-ware via swarm-array computing

The work reported in this paper proposes Swarm-Array computing, a novel technique inspired by swarm robotics, and built on the foundations of autonomic and parallel computing. The approach aims to apply autonomic computing constructs to parallel computing systems and in effect achieve the self-ware objectives that describe self-managing systems. The constitution of swarm-array computing comprising four constituents, namely the computing system, the problem/task, the swarm and the landscape is considered. Approaches that bind these constituents together are proposed. Space applications employing FPGAs are identified as a potential area for applying swarm-array computing for building reliable systems. The feasibility of a proposed approach is validated on the SeSAm multi-agent simulator and landscapes are generated using the MATLAB toolkit.

A review and implementation of swarm pattern formation and transformation models

Purpose: The purpose of this paper is to address a classic problem – pattern formation identified by researchers in the area of swarm robotic systems – and is also motivated by the need for mathematical foundations in swarm systems. Design/methodology/approach: The work is separated out as inspirations, applications, definitions, challenges and classifications of pattern formation in swarm systems based on recent literature. Further, the work proposes a mathematical model for swarm pattern formation and transformation. Findings: A swarm pattern formation model based on mathematical foundations and macroscopic primitives is proposed. A formal definition for swarm pattern transformation and four special cases of transformation are introduced. Two general methods for transforming patterns are investigated and a comparison of the two methods is presented. The validity of the proposed models, and the feasibility of the methods investigated are confirmed on the Traer Physics and Processing environment. Originality/value: This paper helps in understanding the limitations of existing research in pattern formation and the lack of mathematical foundations for swarm systems. The mathematical model and transformation methods introduce two key concepts, namely macroscopic primitives and a mathematical model. The exercise of implementing the proposed models on physics simulator is novel.

Achieving intelligent agents and its feasibility in swarm-array computing

The work reported in this paper proposes ‘Intelligent Agents’, a Swarm-Array computing approach focused to apply autonomic computing concepts to parallel computing systems and build reliable systems for space applications. Swarm-array computing is a robotics a swarm robotics inspired novel computing approach considered as a path to achieve autonomy in parallel computing systems. In the intelligent agent approach, a task to be executed on parallel computing cores is considered as a swarm of autonomous agents. A task is carried to a computing core by carrier agents and can be seamlessly transferred between cores in the event of a predicted failure, thereby achieving self-* objectives of autonomic computing. The approach is validated on a multi-agent simulator.

Nonlinear channel equalizer design using directional evolutionary multi-objective optimization

In this paper, a new equalizer learning scheme is introduced based on the algorithm of the directional evolutionary multi-objective optimization (EMOO). Whilst nonlinear channel equalizers such as the radial basis function (RBF) equalizers have been widely studied to combat the linear and nonlinear distortions in the modern communication systems, most of them do not take into account the equalizers' generalization capabilities. In this paper, equalizers are designed aiming at improving their generalization capabilities. It is proposed that this objective can be achieved by treating the equalizer design problem as a multi-objective optimization (MOO) problem, with each objective based on one of several training sets, followed by deriving equalizers with good capabilities of recovering the signals for all the training sets. Conventional EMOO which is widely applied in the MOO problems suffers from disadvantages such as slow convergence speed. Directional EMOO improves the computational efficiency of the conventional EMOO by explicitly making use of the directional information. The new equalizer learning scheme based on the directional EMOO is applied to the RBF equalizer design. Computer simulation demonstrates that the new scheme can be used to derive RBF equalizers with good generalization capabilities, i.e., good performance on predicting the unseen samples.

Epigenomic plasticity within populations: its evolutionary significance and potential

Epigenetics has progressed rapidly from an obscure quirk of heredity into a data-heavy ‘omic’ science. Our understanding of the molecular mechanisms of epigenomic regulation, and the extent of its importance in nature, are far from complete, but in spite of such drawbacks, population-level studies are extremely valuable: epigenomic regulation is involved in several processes central to evolutionary biology including phenotypic plasticity, evolvability and the mediation of intragenomic conflicts. The first studies of epigenomic variation within populations suggest high levels of phenotypically relevant variation, with the patterns of epigenetic regulation varying between individuals and genome regions as well as with environment. Epigenetic mechanisms appear to function primarily as genome defences, but result in the maintenance of plasticity together with a degree of buffering of developmental programmes; periodic breakdown of epigenetic buffering could potentially cause variation in rates of phenotypic evolution.

The evolutionary ecology of resistance to parasitoids by Drosophila

Parasitoids are the most important natural enemies of many insect species. Larvae of many Drosophila species can defend themselves against attack by parasitoids through a cellular immune response called encapsulation. The paper reviews recent studies of the evolutionary biology and ecological genetics of resistance in Drosophila, concentrating on D. melanogaster. The physiological basis of encapsulation, and the genes known to interfere with resistance are briefly summarized. Evidence for within- and between-population genetic variation in resistance from isofemale line, artificial selection and classical genetic studies are reviewed. There is now firm evidence that resistance is costly to Drosophila, and the nature of this cost is discussed, and the possibility that it may involve a reduction in metabolic rate considered. Comparative data on encapsulation and metabolic rates across seven Drosophila species provides support for this hypothesis. Finally, the possible population and community ecological consequences of evolution in the levels of host resistance are examined.

Assessing the impact of nano- and micro-scale zerovalent iron particles on soil microbial activities: Particle reactivity interferes with assay conditions and interpretation of genuine microbial effects

The effects of nano-scale and micro-scale zerovalent iron (nZVI and mZVI) particles on general (dehydrogenase and hydrolase) and specific (ammonia oxidation potential, AOP) activities mediated by the microbial community in an uncontaminated soil were examined. nZVI (diameter 12.5 nm; 10 mg gÿ1 soil)apparently inhibited AOP and nZVI and mZVI apparently stimulated dehydrogenase activity but had minimal influence on hydrolase activity. Sterile experiments revealed that the apparent inhibition of AOP could not be interpreted as such due to the confounding action of the particles, whereas, the nZVIenhanced dehydrogenase activity could represent the genuine response of a stimulated microbial population or an artifact of ZVI reactivity. Overall, there was no evidence for negative effects of nZVI or mZVI on the processes studied. When examining the impact of redox active particles such as ZVI on microbial oxidation–reduction reactions, potential confounding effects of the test particles on assay conditions should be considered.

Integrating features for man-made target tracking from FLIR image sequence using particle filter

A new man-made target tracking algorithm integrating features from (Forward Looking InfraRed) image sequence is presented based on particle filter. Firstly, a multiscale fractal feature is used to enhance targets in FLIR images. Secondly, the gray space feature is defined by Bhattacharyya distance between intensity histograms of the reference target and a sample target from MFF (Multi-scale Fractal Feature) image. Thirdly, the motion feature is obtained by differencing between two MFF images. Fourthly, a fusion coefficient can be automatically obtained by online feature selection method for features integrating based on fuzzy logic. Finally, a particle filtering framework is developed to fulfill the target tracking. Experimental results have shown that the proposed algorithm can accurately track weak or small man-made target in FLIR images with complicated background. The algorithm is effective, robust and satisfied to real time tracking.