Comparing Evolutionary Algorithms and Particle Filters for Markerless Human Motion Capture

Markerless Human Motion Capture is the problem of determining the joints’ angles of a three-dimensional articulated body model that best matches current and past observations acquired by video cameras. The problem of Markerless Human Motion Capture is high-dimensional and requires the use of models with a considerable number of degrees of freedom to appropriately adapt to the human anatomy. Particle filters have become the most popular approach for Markerless Human Motion Capture, despite their difficulty to cope with high-dimensional problems. Although several solutions have been proposed to improve their performance, they still suffer from the curse of dimensionality. As a consequence, it is normally required to impose mobility limitations in the body models employed, or to exploit the hierarchical nature of the human skeleton by partitioning the problem into smaller ones. Evolutionary algorithms, though, are powerful methods for solving continuous optimization problems, specially the high-dimensional ones. Yet, few works have tackled Markerless Human Motion Capture using them. This paper evaluates the performance of three of the most competitive algorithms in continuous optimization – Covariance Matrix Adaptation Evolutionary Strategy, Differential Evolution and Particle Swarm Optimization – with two of the most relevant particle filters proposed in the literature, namely the Annealed Particle Filter and the Partitioned Sampling Annealed Particle Filter. The algorithms have been experimentally compared in the public dataset HumanEva-I by employing two body models with different complexities. Our work also analyzes the performance of the algorithms in hierarchical and holistic approaches, i.e., with and without partitioning the search space. Non-parametric tests run on the results have shown that: (i) the evolutionary algorithms employed outperform their particle filter counterparts in all the cases tested; (ii) they can deal with high-dimensional models thus leading to better accuracy; and (iii) the hierarchical strategy surpasses the holistic one

Evaluation of genetic improvement programmes using multiple ovulation and embryo transfer in dairy cattle

SIGLEAvailable from British Library Document Supply Centre- DSC:D94937 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

IST Austria Thesis

Hybrid zones represent evolutionary laboratories, where recombination brings together alleles in combinations which have not previously been tested by selection. This provides an excellent opportunity to test the effect of molecular variation on fitness, and how this variation is able to spread through populations in a natural context. The snapdragon Antirrhinum majus is polymorphic in the wild for two loci controlling the distribution of yellow and magenta floral pigments. Where the yellow A. m. striatum and the magenta A. m. pseudomajus meet along a valley in the Spanish Pyrenees they form a stable hybrid zone Alleles at these loci recombine to give striking transgressive variation for flower colour. The sharp transition in phenotype over ~1km implies strong selection maintaining the hybrid zone. An indirect assay of pollinator visitation in the field found that pollinators forage in a positive-frequency dependent manner on Antirrhinum, matching previous data on fruit set. Experimental arrays and paternity analysis of wild-pollinated seeds demonstrated assortative mating for pigmentation alleles, and that pollinator behaviour alone is sufficient to explain this pattern. Selection by pollinators should be sufficiently strong to maintain the hybrid zone, although other mechanisms may be at work. At a broader scale I examined evolutionary transitions between yellow and anthocyanin pigmentation in the tribe Antirrhinae, and found that selection has acted strate that pollinators are a major determinant of reproductive success and mating patterns in wild Antirrhinum

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field

Interaction Topologies and Information Flow

Networks are ubiquitous, underlying systems as diverse as the Internet, food webs, societal interactions, the cell, and the brain. Of crucial importance is the coupling of network structure with system dynamics, and much recent attention has focused on how information, such as pathogens, mutations, or ideas, ow through networks. In this dissertation, we advance the understanding of how network structure a ects information ow in two important classes of models. The rst is an independent interaction model, which is used to investigate the propagation of advantageous alleles in evolutionary algorithms. The second is a threshold model, which is used to study the dissemination of ideas, fads, and innovations throughout populations. This journal-format dissertation comprises three interrelated studies, in which we investigate the in uence of network structure on the dynamical properties of information ow. In the rst study, we develop an analytical technique to approximate system dynamics in arbitrarily structured regular interaction topologies. In the second study, we investigate the ow of advantageous alleles in degree-correlated scale-free population structures, and provide a simple topological metric for assessing the selective pressures induced by these networks. In the third study, we characterize the conditions in which global information cascades occur in threshold models of binary decisions with externalities, structured on degree-correlated Poisson-distributed random networks

Genetic diversity and gain : the concept of a status number

A trade-off always tends to exist involving genetic gains and selection intensity, on the one hand, and the remaining effective population size (usually known as Ne), on the other. A new approach is presented and analysed for different breeding situations, using stochastic simulations, in terms of mating designs and subline sizes, guiding breeders through a new concept of status number (Ns) and its trade-off with gain. Status number is defined as half the inverse of the average coancestry and depicts the current state of the population. The status number concept can easily be applied to deployment of different genotypes with unequal representation. Breeding schemes with small breeding groups are slightly more efficient in preserving status number through multiple generations than breeding schemes with large groups. Medium- to large-size breeding groups showed a comparatively small reduction in aggregated status number over generations but showed greater increases in gain compared with small groups. Inbreeding in small elites becomes so great that it is likely to cause fertility problems and disturb selection considerably. Small breeding groups will probably not be useful for a sustainable long-term breeding strategy. Substantial benefits on status number for subdividing the population into small breeding groups will only be seen after numerous generations. Selection schemes that maximise gain by unrestricted combined index selection will result in rapid inbreeding, and may not be sustainable in the long term. Selection procedures that place less emphasis on family information would best meet long-term diversity targets. However, gains may be too low for mating systems and selection procedures that do not include a between-family component, especially with low heritabilities. This is a good reason for using a large number of families as founders of the breeding population. Going from selection within only 0.5 or 1 available cross per parent per generation (made equivalent to within-family selection) to 2.5 crosses per parent (restricting the number of individuals chosen per full-sib family) resulted in substantial increases in genetic gain, depending on heritability. However, increasing the number of crosses per parent up to 2.5 does carry a modest penalty of increased coefficient of inbreeding and reduced status number. Higher levels of gain per unit of status number loss are obtained with a conservative within family selection strategy but to reach the same level of gain more cycles of breeding will be required. Effects of departures from assumptions (zero inbreeding coefficient and coancestry for the founders, genes being independently assorted, no mutation and interactions, or combinations from departures of the neutrality assumption) , singly and in various combinations will occur, meaning that calculations and predictions based on pedigrees will be biased. Future work will require modelling the effects for departures from the idealised assumptions and laboratory-based quantification of departures from some key assumptions

Genetic diversity and gain : the concept of a status number

A trade-off always tends to exist involving genetic gains and selection intensity, on the one hand, and the remaining effective population size (usually known as Ne), on the other. A new approach is presented and analysed for different breeding situations, using stochastic simulations, in terms of mating designs and subline sizes, guiding breeders through a new concept of status number (Ns) and its trade-off with gain. Status number is defined as half the inverse of the average coancestry and depicts the current state of the population. The status number concept can easily be applied to deployment of different genotypes with unequal representation. Breeding schemes with small breeding groups are slightly more efficient in preserving status number through multiple generations than breeding schemes with large groups. Medium- to large-size breeding groups showed a comparatively small reduction in aggregated status number over generations but showed greater increases in gain compared with small groups. Inbreeding in small elites becomes so great that it is likely to cause fertility problems and disturb selection considerably. Small breeding groups will probably not be useful for a sustainable long-term breeding strategy. Substantial benefits on status number for subdividing the population into small breeding groups will only be seen after numerous generations. Selection schemes that maximise gain by unrestricted combined index selection will result in rapid inbreeding, and may not be sustainable in the long term. Selection procedures that place less emphasis on family information would best meet long-term diversity targets. However, gains may be too low for mating systems and selection procedures that do not include a between-family component, especially with low heritabilities. This is a good reason for using a large number of families as founders of the breeding population. Going from selection within only 0.5 or 1 available cross per parent per generation (made equivalent to within-family selection) to 2.5 crosses per parent (restricting the number of individuals chosen per full-sib family) resulted in substantial increases in genetic gain, depending on heritability. However, increasing the number of crosses per parent up to 2.5 does carry a modest penalty of increased coefficient of inbreeding and reduced status number. Higher levels of gain per unit of status number loss are obtained with a conservative within family selection strategy but to reach the same level of gain more cycles of breeding will be required. Effects of departures from assumptions (zero inbreeding coefficient and coancestry for the founders, genes being independently assorted, no mutation and interactions, or combinations from departures of the neutrality assumption) , singly and in various combinations will occur, meaning that calculations and predictions based on pedigrees will be biased. Future work will require modelling the effects for departures from the idealised assumptions and laboratory-based quantification of departures from some key assumptions