Dynamic coast control of train movement with genetic algorithm

The railway service is now the major transportation means in most of the countries around the world. With the increasing population and expanding commercial and industrial activities, a high quality of railway service is the most desirable. Train service usually varies with the population activities throughout a day and train coordination and service regulation are then expected to meet the daily passengers' demand. Dwell time control at stations and fixed coasting point in an inter-station run are the current practices to regulate train service in most metro railway systems. However, a flexible and efficient train control and operation is not always possible. To minimize energy consumption of train operation and make certain compromises on the train schedule, coast control is an economical approach to balance run-time and energy consumption in railway operation if time is not an important issue, particularly at off-peak hours. The capability to identify the starting point for coasting according to the current traffic conditions provides the necessary flexibility for train operation. This paper presents an application of genetic algorithms (GA) to search for the appropriate coasting point(s) and investigates the possible improvement on fitness of genes. Single and multiple coasting point control with simple GA are developed to attain the solutions and their corresponding train movement is examined. Further, a hierarchical genetic algorithm (HGA) is introduced here to identify the number of coasting points required according to the traffic conditions, and Minimum-Allele-Reserve-Keeper (MARK) is adopted as a genetic operator to achieve fitter solutions.

Coast control of train movement with genetic algorithms

Railway service is now the major transportation means in most of the countries around the world. With the increasing population and expanding commercial and industrial activities, a high quality of railway service is the most desirable. We present an application of genetic algorithms (GA) to search for the appropriate coasting point(s) and investigate the possible improvement on fitness of genes. Single and multiple coasting point control with simple GA are developed to attain the solutions and their corresponding train movement is examined. The multiple coasting point control with hierarchical genetic algorithm (HGA) is then proposed to integrate the determination of the number of coasting points.

Coasting control of train operation by genetic algorithm

Balancing between the provision of high quality of service and running within a tight budget is one of the biggest challenges for most metro railway operators around the world. Conventionally, one possible approach for the operator to adjust the time schedule is to alter the stop time at stations, if other system constraints, such as traction equipment characteristic, are not taken into account. Yet it is not an effective, flexible and economical method because the run-time of a train simply cannot be extended without limitation, and a balance between run-time and energy consumption has to be maintained. Modification or installation of a new signalling system not only increases the capital cost, but also affects the normal train service. Therefore, in order to procure a more effective, flexible and economical means to improve the quality of service, optimisation of train performance by coasting point identification has become more attractive and popular. However, identifying the necessary starting points for coasting under the constraints of current service conditions is no simple task because train movement is attributed by a large number of factors, most of which are non-linear and inter-dependent. This paper presents an application of genetic algorithms (GA) to search for the appropriate coasting points and investigates the possible improvement on computation time and fitness of genes.

Genetic resources in wild and cultured stocks of the Asian mud crab, Scylla paramamosain

The mud crab (Scylla spp.) aquaculture industry has expanded rapidly in recent years in many countries in the Indo - West Pacific (IWP) region as an alternative to marine shrimp culture because of significant disease outbreaks and associated failures of many shrimp culture industries in the region. Currently, practices used to produce and manage breeding crabs in hatcheries may compromise levels of genetic diversity, ultimately compromising growth rates, disease resistance and stock productivity. Therefore, to avoid “genetic pollution” and its harmful effects and to promote further development of mud crab aquaculture and fisheries in a sustainable way, a greater understanding of the genetic attributes of wild and cultured mud crab stocks is required. Application of these results can provide benefits for managing wild and cultured Asian mud crab populations for multiple purposes including for commercial production, recreation and conservation and to increase profitability and sustainability of newly emerging crab culture industries. Phylogeographic patterns and the genetic structure of Asian mud crab populations across the IWP were assessed to determine if they were concordant with those of other widespread taxa possessing pelagic larvae of relatively long duration. A 597 bp fragment of the mitochondrial DNA COI gene was amplified and screened for variation in a total of 297 individuals of S. paramamosain from six sampling sites across the species’ natural geographical distribution in the IWP and 36 unique haplotypes were identified. Haplotype diversities per site ranged from 0.516 to 0.879. Nucleotide diversity estimates among haplotypes were 0.11% – 0.48%. Maximum divergence observed among S. paramamosain samples was 1.533% and samples formed essentially a single monophyletic group as no obvious clades were related to geographical location of sites. A weak positive relationship was observed however, between genetic distance and geographical distance among sites. Microsatellite markers were then used to assess contemporary gene flow and population structure in Asian mud crab populations sampled across their natural distribution in the IWP. Eight microsatellite loci were screened in sampled S. paramamosain populations and all showed high allelic diversity at all loci in sampled populations. In total, 344 individuals were analysed, and 304 microsatellite alleles were found across the 8 loci. The mean number of alleles per locus at each site ranged from 20.75 to 28.25. Mean allelic richness per site varied from 17.2 to 18.9. All sites showed high levels of heterozygosity as average expected heterozygosities for all loci ranged from 0.917 – 0.953 while mean observed heterozygosity ranged from 0.916 – 0.959. Allele diversities were similar at all sites and across all loci. The results did not show any evidence for major differences in allele frequencies among sites and patterns of allele frequencies were very similar in all populations across all loci. Estimates of population differentiation (FST) were relatively low and most probably largely reflect intra – individual variation for very highly variable loci. Results from nDNA analysis showed evidence for only very limited population genetic structure among sampled S. paramamosain, and a positive and significant association for genetic and geographical distance among sample sites. Microsatellite markers were then employed to determine if adequate levels of genetic diversity has been captured in crab hatcheries for the breeding cycle. The results showed that all microsatellite loci were polymorphic in hatchery samples. Culture populations were in general, highly genetically depauperate, compared with comparable wild populations, with only 3 to 8 alleles recorded for the same loci set per population. In contrast, very high numbers of alleles per locus were found in reference wild S. paramamosain populations, which ranged from 18 to 46 alleles per locus per population. In general, this translates into a 3 to 10 fold decline in mean allelic richness per locus in all culture stocks compared with wild reference counterparts. Furthermore, most loci in all cultured S. paramamosain samples showed departures from HWE equilibrium. Allele frequencies were very different in culture samples from that present in comparable wild reference samples and this in particular, was reflected in a large decline in allele diversity per locus. The pattern observed was best explained by significant impacts of breeding practices employed in hatcheries rather than natural differentiation among wild populations used as the source of brood stock. Recognition of current problems and management strategies for the species both for the medium and long-term development of the new culture industry are discussed. The priority research to be undertaken over the medium term for S. paramamosain should be to close the life cycle fully to allow individuals to be bred on demand and their offspring equalised to control broodstock reproductive contributions. Establishing a broodstock register and pedigree mating system will be required before any selection program is implemented. This will ensure that sufficient genetic variation will be available to allow genetic gains to be sustainably achieved in a future stock improvement program. A fundamental starting point to improve hatchery practices will be to encourage farmers and hatchery managers to spawn more females in their hatcheries as it will increase background genetic diversity in culture stocks. Combining crablet cohorts from multiple hatcheries into a single cohort for supply to farmers or rotation of breeding females regularly in hatcheries will help to address immediate genetic diversity problems in culture stocks. Application of these results can provide benefits for managing wild and cultured Asian mud crab populations more efficiently. Over the long-term, application of data on genetic diversity in wild and cultured stocks of Asian mud crab will contribute to development of sustainable and productive culture industries in Vietnam and other countries in the IWP and can contribute towards conservation of wild genetic resources.

Impacts of traditional husbandry practices on exploitable levels of genetic diversity in cultured 'Tra' catfish (Pangasianodon hypophthalmus) in the Mekong Delta, Vietnam

Sutchi catfish (Pangasianodon hypophthalmus) – known more universally by the Vietnamese name ‘Tra’ is an economically important freshwater fish in the Mekong Delta in Vietnam that constitutes an important food resource. Artificial propagation technology for Tra catfish has only recently been developed along the main branches of the Mekong River where more than 60% of the local human population participate in fishing or aquaculture. Extensive support for catfish culture in general, and that of Tra (P. hypophthalmus) in particular, has been provided by the Vietnamese government to increase both the scale of production and to develop international export markets. In 2006, total Vietnamese catfish exports reached approximately 286,602 metric tons (MT) and were valued at 736.87 $M with a number of large new export destinations being developed. Total value of production from catfish culture has been predicted to increase to approximately USD 1 billion by 2020. While freshwater catfish culture in Vietnam has a promising future, concerns have been raised about long-term quality of fry and the effectiveness of current brood stock management practices, issues that have been largely neglected to date. In this study, four DNA markers (microsatellite loci: CB4, CB7, CB12 and CB13) that were developed specifically for Tra (P. hypophthalmus) in an earlier study were applied to examine the genetic quality of artificially propagated Tra fry in the Mekong Delta in Vietnam. The goals of the study were to assess: (i) how well available levels of genetic variation in Tra brood stock used for artificial propagation in the Mekong Delta of Vietnam (breeders from three private hatcheries and Research Institute of Aquaculture No2 (RIA2) founders) has been conserved; and (ii) whether or not genetic diversity had declined significantly over time in a stock improvement program for Tra catfish at RIA2. A secondary issue addressed was how genetic markers could best be used to assist industry development. DNA was extracted from fins of catfish collected from the two main branches of the Mekong River inf Vietnam, three private hatcheries and samples from the Tra improvement program at RIA2. Study outcomes: i) Genetic diversity estimates for Tra brood stock samples were similar to, and slightly higher than, wild reference samples. In addition, the relative contribution by breeders to fry in commercial private hatcheries strongly suggest that the true Ne is likely to be significantly less than the breeder numbers used; ii) in a stock improvement program for Tra catfish at RIA2, no significant differences were detected in gene frequencies among generations (FST=0.021, P=0.036>0.002 after Bonferroni correction); and only small differences were observed in alleles frequencies among sample populations. To date, genetic markers have not been applied in the Tra catfish industry, but in the current project they were used to evaluate the levels of genetic variation in the Tra catfish selective breeding program at RIA2 and to undertake genetic correlations between genetic marker and trait variation. While no associations were detected using only four loci, they analysis provided training in the practical applications of the use of molecular markers in aquaculture in general, and in Tra culture, in particular.

Perturbing event logs to identify cost reduction opportunities: a genetic algorithm-based approach

Organisations are constantly seeking new ways to improve operational efficiencies. This research study investigates a novel way to identify potential efficiency gains in business operations by observing how they are carried out in the past and then exploring better ways of executing them by taking into account trade-offs between time, cost and resource utilisation. This paper demonstrates how they can be incorporated in the assessment of alternative process execution scenarios by making use of a cost environment. A genetic algorithm-based approach is proposed to explore and assess alternative process execution scenarios, where the objective function is represented by a comprehensive cost structure that captures different process dimensions. Experiments conducted with different variants of the genetic algorithm evaluate the approach's feasibility. The findings demonstrate that a genetic algorithm-based approach is able to make use of cost reduction as a way to identify improved execution scenarios in terms of reduced case durations and increased resource utilisation. The ultimate aim is to utilise cost-related insights gained from such improved scenarios to put forward recommendations for reducing process-related cost within organisations.

A random key genetic algorithm for live migration of multiple virtual machines in data centers

Live migration of multiple Virtual Machines (VMs) has become an integral management activity in data centers for power saving, load balancing and system maintenance. While state-of-the-art live migration techniques focus on the improvement of migration performance of an independent single VM, only a little has been investigated to the case of live migration of multiple interacting VMs. Live migration is mostly influenced by the network bandwidth and arbitrarily migrating a VM which has data inter-dependencies with other VMs may increase the bandwidth consumption and adversely affect the performances of subsequent migrations. In this paper, we propose a Random Key Genetic Algorithm (RKGA) that efficiently schedules the migration of a given set of VMs accounting both inter-VM dependency and data center communication network. The experimental results show that the RKGA can schedule the migration of multiple VMs with significantly shorter total migration time and total downtime compared to a heuristic algorithm.

Efficiency of parallelisation of genetic algorithms in the data analysis context

Most real-life data analysis problems are difficult to solve using exact methods, due to the size of the datasets and the nature of the underlying mechanisms of the system under investigation. As datasets grow even larger, finding the balance between the quality of the approximation and the computing time of the heuristic becomes non-trivial. One solution is to consider parallel methods, and to use the increased computational power to perform a deeper exploration of the solution space in a similar time. It is, however, difficult to estimate a priori whether parallelisation will provide the expected improvement. In this paper we consider a well-known method, genetic algorithms, and evaluate on two distinct problem types the behaviour of the classic and parallel implementations.

Revising history for cost-informed process improvement

Organisations are constantly seeking new ways to improve operational efficiencies. This study investigates a novel way to identify potential efficiency gains in business operations by observing how they were carried out in the past and then exploring better ways of executing them by taking into account trade-offs between time, cost and resource utilisation. This paper demonstrates how these trade-offs can be incorporated in the assessment of alternative process execution scenarios by making use of a cost environment. A number of optimisation techniques are proposed to explore and assess alternative execution scenarios. The objective function is represented by a cost structure that captures different process dimensions. An experimental evaluation is conducted to analyse the performance and scalability of the optimisation techniques: integer linear programming (ILP), hill climbing, tabu search, and our earlier proposed hybrid genetic algorithm approach. The findings demonstrate that the hybrid genetic algorithm is scalable and performs better compared to other techniques. Moreover, we argue that the use of ILP is unrealistic in this setup and cannot handle complex cost functions such as the ones we propose. Finally, we show how cost-related insights can be gained from improved execution scenarios and how these can be utilised to put forward recommendations for reducing process-related cost and overhead within organisations.

Rapid and efficient screening of a Representational Difference Analysis library using reverse Southern hybridisation: Identification of genetic differences between Haemophilus parasuis isolates

Representational Difference Analysis (RDA) is an established technique used for isolation of specific genetic differences between or within bacterial species. This method was used to investigate the genetic basis of serovar-specificity and the relationship between serovar and virulence in Haemophilus parasuis. An RDA clone library of 96 isolates was constructed using H. parasuis strains H425(P) (serovar 12) and HS1967 (serovar 4). To screen such a large clone library to determine which clones are strain-specific would typically involved separately labelling each clone for use in Southern hybridisation against genomic DNA from each of the strains. In this study, a novel application of reverse Southern hybridisation was used to screen the RDA library: genomic DNA from each strain was labelled and used to probe the library to identify strain-specific clones. This novel approach represents a significant improvement in methodology that is rapid and efficient.

Environment characterization as an aid to wheat improvement: interpreting genotype-environment interactions by modelling water-deficit patterns in North-Eastern Australia.

Genotype-environment interactions (GEI) limit genetic gain for complex traits such as tolerance to drought. Characterization of the crop environment is an important step in understanding GEI. A modelling approach is proposed here to characterize broadly (large geographic area, long-term period) and locally (field experiment) drought-related environmental stresses, which enables breeders to analyse their experimental trials with regard to the broad population of environments that they target. Water-deficit patterns experienced by wheat crops were determined for drought-prone north-eastern Australia, using the APSIM crop model to account for the interactions of crops with their environment (e.g. feedback of plant growth on water depletion). Simulations based on more than 100 years of historical climate data were conducted for representative locations, soils, and management systems, for a check cultivar, Hartog. The three main environment types identified differed in their patterns of simulated water stress around flowering and during grain-filling. Over the entire region, the terminal drought-stress pattern was most common (50% of production environments) followed by a flowering stress (24%), although the frequencies of occurrence of the three types varied greatly across regions, years, and management. This environment classification was applied to 16 trials relevant to late stages testing of a breeding programme. The incorporation of the independently-determined environment types in a statistical analysis assisted interpretation of the GEI for yield among the 18 representative genotypes by reducing the relative effect of GEI compared with genotypic variance, and helped to identify opportunities to improve breeding and germplasm-testing strategies for this region.

Integrating sorghum whole genome sequence information with a compendium of sorghum QTL studies reveals uneven distribution of QTL and of gene-rich regions with significant implications for crop improvement.

A comprehensive analysis was conducted using 48 sorghum QTL studies published from 1995 to 2010 to make information from historical sorghum QTL experiments available in a form that could be more readily used by sorghum researchers and plant breeders. In total, 771 QTL relating to 161 unique traits from 44 studies were projected onto a sorghum consensus map. Confidence intervals (CI) of QTL were estimated so that valid comparisons could be made between studies. The method accounted for the number of lines used and the phenotypic variation explained by individual QTL from each study. In addition, estimated centimorgan (cM) locations were calculated for the predicted sorghum gene models identified in Phytozome (JGI GeneModels SBI v1.4) and compared with QTL distribution genome-wide, both on genetic linkage (cM) and physical (base-pair/bp) map scales. QTL and genes were distributed unevenly across the genome. Heterochromatic enrichment for QTL was observed, with approximately 22% of QTL either entirely or partially located in the heterochromatic regions. Heterochromatic gene enrichment was also observed based on their predicted cM locations on the sorghum consensus map, due to suppressed recombination in heterochromatic regions, in contrast to the euchromatic gene enrichment observed on the physical, sequence-based map. The finding of high gene density in recombination-poor regions, coupled with the association with increased QTL density, has implications for the development of more efficient breeding systems in sorghum to better exploit heterosis. The projected QTL information described, combined with the physical locations of sorghum sequence-based markers and predicted gene models, provides sorghum researchers with a useful resource for more detailed analysis of traits and development of efficient marker-assisted breeding strategies.

GENEHUNTER: your 'one-stop shop' for statistical genetic analysis?

The past decade has brought a proliferation of statistical genetic (linkage) analysis techniques, incorporating new methodology and/or improvement of existing methodology in gene mapping, specifically targeted towards the localization of genes underlying complex disorders. Most of these techniques have been implemented in user-friendly programs and made freely available to the genetics community. Although certain packages may be more 'popular' than others, a common question asked by genetic researchers is 'which program is best for me?'. To help researchers answer this question, the following software review aims to summarize the main advantages and disadvantages of the popular GENEHUNTER package.

Inventories and significance of the genetic resources of an African mahogany species (Khaya senegalensis (Desr.) A. Juss.) assembled and further developed in Australia.

The forest tree species Khaya senegalensis (Desr.) A. Juss. occurs in a belt across 20 African countries from Senegal-Guinea to Sudan-Uganda where it is a highly important resource. However, it is listed as Vulnerable (IUCN 2015-3). Since introduction in northern Australia around 1959, the species has been planted widely, yielding high-value products. The total area of plantations of the species in Australia exceeds 15,000 ha, mostly planted in the Northern Territory since 2006, and includes substantial areas across 60-70 woodlots and industrial plantations established in north-eastern Queensland since the early-1990s and during 2005-2007 respectively. Collaborative conservation and tree improvement by governments began in the Northern Territory and Queensland in 2001 based on provenance and other trials of the 1960s-1970s. This work has developed a broad base of germplasm in clonal seed orchards, hedge gardens and trials (clone and progeny). Several of the trials were established collaboratively on private land. Since the mid-2000s, commercial growers have introduced large numbers of provenance-bulk and individual-tree seedlots to establish industrial plantations and trials, several of the latter in collaboration with the Queensland Government. Provenance bulks (>140) and families (>400) from 17 African countries are established in Australia, considered the largest genetic base of the species in a single country outside Africa. Recently the annual rate of industrial planting of the species in Australia has declined, and R&D has been suspended by governments and reduced by the private sector. However, new commercial plantings in the Northern Territory and Queensland are proposed. In domesticating a species, the strategic importance of a broad genetic base is well known. The wide range of first- and advanced-generation germplasm of the species established in northern Australia and documented in this paper provides a sound basis for further domestication and industrial plantation and woodlot expansion, when investment conditions are favourable

Evaluation of a 7-gene genetic profile for athletic endurance phenotype in Ironman championship triathletes

Polygenic profiling has been proposed for elite endurance performance, using an additive model determining the proportion of optimal alleles in endurance athletes. To investigate this model’s utility for elite triathletes, we genotyped seven polymorphisms previously associated with an endurance polygenic profile (ACE Ins/Del, ACTN3 Arg577Ter, AMPD1 Gln12Ter, CKMM 1170bp/985+185bp, HFE His63Asp, GDF8 Lys153Arg and PPARGC1A Gly482Ser) in a cohort of 196 elite athletes who participated in the 2008 Kona Ironman championship triathlon. Mean performance time (PT) was not significantly different in individual marker analysis. Age, sex, and continent of origin had a significant influence on PT and were adjusted for. Only the AMPD1 endurance-optimal Gln allele was found to be significantly associated with an improvement in PT (model p=5.79 x 10-17, AMPD1 genotype p=0.01). Individual genotypes were combined into a total genotype score (TGS); TGS distribution ranged from 28.6 to 92.9, concordant with prior studies in endurance athletes (mean±SD: 60.75±12.95). TGS distribution was shifted toward higher TGS in the top 10% of athletes, though the mean TGS was not significantly different (p=0.164) and not significantly associated with PT even when adjusted for age, sex, and origin. Receiver operating characteristic curve analysis determined that TGS alone could not significantly predict athlete finishing time with discriminating sensitivity and specificity for three outcomes (less than median PT, less than mean PT, or in the top 10%), though models with the age, sex, continent of origin, and either TGS or AMPD1 genotype could. These results suggest three things: that more sophisticated genetic models may be necessary to accurately predict athlete finishing time in endurance events; that non-genetic factors such as training are hugely influential and should be included in genetic analyses to prevent confounding; and that large collaborations may be necessary to obtain sufficient sample sizes for powerful and complex analyses of endurance performance.