Epipolar geometry estimation based on evolutionary agents

This paper presents a novel approach based on the use of evolutionary agents for epipolar geometry estimation. In contrast to conventional nonlinear optimization methods, the proposed technique employs each agent to denote a minimal subset to compute the fundamental matrix, and considers the data set of correspondences as a 1D cellular environment, in which the agents inhabit and evolve. The agents execute some evolutionary behavior, and evolve autonomously in a vast solution space to reach the optimal (or near optima) result. Then three different techniques are proposed in order to improve the searching ability and computational efficiency of the original agents. Subset template enables agents to collaborate more efficiently with each other, and inherit accurate information from the whole agent set. Competitive evolutionary agent (CEA) and finite multiple evolutionary agent (FMEA) apply a better evolutionary strategy or decision rule, and focus on different aspects of the evolutionary process. Experimental results with both synthetic data and real images show that the proposed agent-based approaches perform better than other typical methods in terms of accuracy and speed, and are more robust to noise and outliers.

On the evolutionary status of short-period cataclysmic variables

We present high-speed, three-colour photometry of seven short-period (Porb

A Hybrid Evolutionary Approach to the Nurse Rostering Problem

Nurse rostering is a difficult search problem with many constraints. In the literature, a number of approaches have been investigated including penalty function methods to tackle these constraints within genetic algorithm frameworks. In this paper, we investigate an extension of a previously proposed stochastic ranking method, which has demonstrated superior performance to other constraint handling techniques when tested against a set of constrained optimisation benchmark problems. An initial experiment on nurse rostering problems demonstrates that the stochastic ranking method is better in finding feasible solutions but fails to obtain good results with regard to the objective function. To improve the performance of the algorithm, we hybridise it with a recently proposed simulated annealing hyper-heuristic within a local search and genetic algorithm framework. The hybrid algorithm shows significant improvement over both the genetic algorithm with stochastic ranking and the simulated annealing hyper-heuristic alone. The hybrid algorithm also considerably outperforms the methods in the literature which have the previously best known results.

Evolutionary agglomeration theory: increasing returns, diminishing returns, and the industry life cycle

According to Marshall’s agglomeration theory, Krugman’s New Economic Geography models, and Porter’s cluster policies, firms should receive increasing returns from a trinity of agglomeration economies: a local pool of skilled labour, local supplier linkages, and local knowledge spillovers. Recent evolutionary theories suggest that whether agglomeration economies generate increasing returns or diminishing returns depends on time, and especially the evolution of the industry life cycle. At the start of the twenty-first century, we re-examine Marshall’s trinity of agglomeration economies in the city-region where he discovered them. The econometric results from our multivariate regression models are the polar opposite of Marshall’s. During the later stages of the industry life cycle, Marshall’s agglomeration economies decrease the economic performance of firms and create widespread diminishing returns for the economic development of the city-region, which has evolved to become one of the poorest city-regions in Europe.

High incidence of cryptic repeated elements in microsatellite flanking regions of galatheid genomes and its evolutionary implications

During the development of PCR primer sets for icrosatellite marker loci from enriched genomic libraries for three squat lobster species from Galatheidae (Decapoda: Anomura); Munida rugosa (Fabricius, 1775), M. sarsi (Huus, 1935), and Galathea strigosa (Linnaeus, 1761) (collectively known as squat lobsters), a number of unforeseen problems were encountered. These included PCR amplification failure, lack of amplification consistency, and the amplification of multiple fragments. Careful examination of microsatellite containing sequences revealed the existence of cryptic repeated elements on presumed unique flanking regions. BLAST analysis of these and other VNTR containing sequences (N 5 252) indicates that these cryptic elements can be grouped into families based upon sequence similarities. The unique features characterising these families suggest that different molecular mechanisms are involved. Of particular relevance is the association of microsatellites with mobile elements. This is the first reported observation of this phenomenon in crustaceans, and it also helps to explain why microsatellite primer development in galatheids has been relatively unsuccessful to date. We suggest a number of steps that can be used to identify similar problems in microsatellite marker development for other species, and also alternative approaches for both marker development and for the study of molecular evolution of species characterised by complex genome organisation. More specifically, we argue that new generation sequencing methodologies, which capitalise on parallel and multiplexed sequencing may pave the way forward for future crustacean research.