Comparison of classification ability of hyperball algorithms to neural network and k-nearest neighbour algorithms

The main focus of this thesis is to evaluate and compare Hyperbalilearning algorithm (HBL) to other learning algorithms. In this work HBL is compared to feed forward artificial neural networks using back propagation learning, K-nearest neighbor and 103 algorithms. In order to evaluate the similarity of these algorithms, we carried out three experiments using nine benchmark data sets from UCI machine learning repository. The first experiment compares HBL to other algorithms when sample size of dataset is changing. The second experiment compares HBL to other algorithms when dimensionality of data changes. The last experiment compares HBL to other algorithms according to the level of agreement to data target values. Our observations in general showed, considering classification accuracy as a measure, HBL is performing as good as most ANn variants. Additionally, we also deduced that HBL.:s classification accuracy outperforms 103's and K-nearest neighbour's for the selected data sets.

Evolutionary Origin and Maintenance of Sociality in the Small Carpenter Bees

Many arthropods exhibit behaviours precursory to social life, including adult longevity, parental care, nest loyalty and mutual tolerance, yet there are few examples of social behaviour in this phylum. The small carpenter bees, genus Ceratina, provide important insights into the early stages of sociality. I described the biology and social behaviour of five facultatively social species which exhibit all of the preadaptations for successful group living, yet present ecological and behavioural characteristics that seemingly disfavour frequent colony formation. These species are socially polymorphic with both / solitary and social nests collected in sympatry. Social colonies consist of two adult females, one contributing both foraging and reproductive effort and the second which remains at the nest as a passive guard. Cooperative nesting provides no overt reproductive benefits over solitary nesting, although brood survival tends to be greater in social colonies. Three main theories explain cooperation among conspecifics: mutual benefit, kin selection and manipulation. Lifetime reproductive success calculations revealed that mutual benefit does not explain social behaviour in this group as social colonies have lower per capita life time reproductive success than solitary nests. Genetic pedigrees constructed from allozyme data indicate that kin selection might contribute to the maintenance of social nesting -, as social colonies consist of full sisters and thus some indirect fitness benefits are inherently bestowed on subordinate females as a result of remaining to help their dominant sister. These data suggest that the origin of sociality in ceratinines has principal costs and the great ecological success of highly eusociallineages occurred well after social origins. Ecological constraints such as resource limitation, unfavourable weather conditions and parasite pressure have long been considered some of the most important selective pressures for the evolution of sociality. I assessed the fitness consequences of these three ecological factors for reproductive success of solitary and social colonies and found that nest sites were not limiting, and the frequency of social nesting was consistent across brood rearing seasons. Local weather varied between seasons but was not correlated with reproductive success. Severe parasitism resulted in low reproductive success and total nest failure in solitary nests. Social colonies had higher reproductive success and were never extirpated by parasites. I suggest that social nesting represents a form of bet-hedging. The high frequency of solitary nests suggests that this is the optimal strategy when parasite pressure is low. However, social colonies have a selective advantage over solitary nesting females during periods of extreme parasite pressure. Finally, the small carpenter bees are recorded from all continents except Antarctica. I constructed the first molecular phylogeny of ceratinine bees based on four gene regions of selected species covering representatives from all continents and ecological regions. Maximum parsimony and Bayesian Inference tree topology and fossil dating support an African origin followed by an Old World invasion and New World radiation. All known Old World ceratinines form social colonies while New World species are largely solitary; thus geography and phylogenetic inertia are likely predictors of social evolution in this genus. This integrative approach not only describes the behaviour of several previously unknown or little-known Ceratina species, bu~ highlights the fact that this is an important, though previously unrecognized, model for studying evolutionary transitions from solitary to social behaviour.

A Multi-Objective Genetic Algorithm with Side Effect Machines for Motif Discovery

Understanding the machinery of gene regulation to control gene expression has been one of the main focuses of bioinformaticians for years. We use a multi-objective genetic algorithm to evolve a specialized version of side effect machines for degenerate motif discovery. We compare some suggested objectives for the motifs they find, test different multi-objective scoring schemes and probabilistic models for the background sequence models and report our results on a synthetic dataset and some biological benchmarking suites. We conclude with a comparison of our algorithm with some widely used motif discovery algorithms in the literature and suggest future directions for research in this area.

Plant rhizosphere specificity and variability in the insect and plant adhesins, Madl and Mad2, within the genus Metarhizium suggest plant adaptation as an evolutionary force

Metarhizium is a soil-inhabiting fungus currently used as a biological control agent against various insect species, and research efforts are typically focused on its ability to kill insects. In section 1, we tested the hypothesis that species of Metarhizium are not randomly distributed in soils but show plant rhizosphere-specific associations. Results indicated an association of three Metarhizium species (Metarhizium robertsii, M. brunneum and M. guizhouense) with the rhizosphere of certain types of plant species. M. robertsii was the only species that was found associated with grass roots, suggesting a possible exclusion of M. brunneum and M. guizhouense, which was supported by in vitro experiments with grass root exudate. M. guizhouense and M. brunneum only associated with wildflower rhizosphere when co-occurring with M. robertsii. With the exception of these co-occurrences, M. guizhouense was found to associate exclusively with the rhizosphere of tree species, while M. brunneum was found to associate exclusively with the rhizosphere of shrubs and trees. These associations demonstrate that different species of Metarhizium associate with specific plant types. In section 2, we explored the variation in the insect adhesin, Madl, and the plant adhesin, Mad2, in fourteen isolates of Metarhizium representing seven different species. Analysis of the transcriptional elements within the Mad2 promoter region revealed variable STRE, PDS, degenerative TATA box, and TATA box-like regions. Phylogenetic analysis of 5' EF-Ia, which is used for species identification, as well as Madl and Mad2 sequences demonstrated that the Mad2 phylogeny is more congruent with 5' EF-1a than Madl. This suggests Mad2 has diverged among Metarhizium lineages, contributing to clade- and species-specific variation. While other abiotic and biotic factors cannot be excluded in contributing to divergence, it appears that plant associations have been the driving factor causing divergence among Metarhizium species.

Multi-Objective Genetic Algorithms for the Single Allocation Hub Location Problem

Hub Location Problems play vital economic roles in transportation and telecommunication networks where goods or people must be efficiently transferred from an origin to a destination point whilst direct origin-destination links are impractical. This work investigates the single allocation hub location problem, and proposes a genetic algorithm (GA) approach for it. The effectiveness of using a single-objective criterion measure for the problem is first explored. Next, a multi-objective GA employing various fitness evaluation strategies such as Pareto ranking, sum of ranks, and weighted sum strategies is presented. The effectiveness of the multi-objective GA is shown by comparison with an Integer Programming strategy, the only other multi-objective approach found in the literature for this problem. Lastly, two new crossover operators are proposed and an empirical study is done using small to large problem instances of the Civil Aeronautics Board (CAB) and Australian Post (AP) data sets.

Variability in the Insect and Plant Adhesins, Mad1 and Mad2, within the Fungal Genus Metarhizium Suggest Plant Adaptation as an Evolutionary Force

Several species of the insect pathogenic fungus Metarhizium are associated with certain plant types and genome analyses suggested a bifunctional lifestyle; as an insect pathogen and as a plant symbiont. Here we wanted to explore whether there was more variation in genes devoted to plant association (Mad2) or to insect association (Mad1) overall in the genus Metarhizium. Greater divergence within the genus Metarhizium in one of these genes may provide evidence for whether host insect or plant is a driving force in adaptation and evolution in the genus Metarhizium. We compared differences in variation in the insect adhesin gene, Mad1, which enables attachment to insect cuticle, and the plant adhesin gene, Mad2, which enables attachment to plants. Overall variation for the Mad1 promoter region (7.1%), Mad1 open reading frame (6.7%), and Mad2 open reading frame (7.4%) were similar, while it was higher in the Mad2 promoter region (9.9%). Analysis of the transcriptional elements within the Mad2 promoter region revealed variable STRE, PDS, degenerative TATA box, and TATA box-like regions, while this level of variation was not found for Mad1. Sequences were also phylogenetically compared to EF-1a, which is used for species identification, in 14 isolates representing 7 different species in the genus Metarhizium. Phylogenetic analysis demonstrated that the Mad2 phylogeny is more congruent with 59 EF-1a than Mad1. This would suggest that Mad2 has diverged among Metarhizium lineages, contributing to clade- and species-specific variation, while it appears that Mad1 has been largely conserved. While other abiotic and biotic factors cannot be excluded in contributing to divergence, these results suggest that plant relationships, rather than insect host, have been a major driving factor in the divergence of the genus Metarhizium.

Properties and Algorithms of the KCube Graphs

The KCube interconnection topology was rst introduced in 2010. The KCube graph is a compound graph of a Kautz digraph and hypercubes. Compared with the at- tractive Kautz digraph and well known hypercube graph, the KCube graph could accommodate as many nodes as possible for a given indegree (and outdegree) and the diameter of interconnection networks. However, there are few algorithms designed for the KCube graph. In this thesis, we will concentrate on nding graph theoretical properties of the KCube graph and designing parallel algorithms that run on this network. We will explore several topological properties, such as bipartiteness, Hamiltonianicity, and symmetry property. These properties for the KCube graph are very useful to develop efficient algorithms on this network. We will then study the KCube network from the algorithmic point of view, and will give an improved routing algorithm. In addition, we will present two optimal broadcasting algorithms. They are fundamental algorithms to many applications. A literature review of the state of the art network designs in relation to the KCube network as well as some open problems in this field will also be given.

Predicting Physical, Verbal, Social, Racial, and Sexual Bullying with Individual and Environmental Factors from an Evolutionary Ecological Framework

Evidence exists for subtypes of bullying, but there is a lack of studies simultaneously investigating the factors that influence each subtype. The purpose of my thesis was to investigate how individual and environmental factors independently and interactively predict physical, verbal, social, racial, and sexual bullying using an evolutionary ecological framework. Adolescents (N = 225, M = 14.05, SD = 1.54) completed self-reports on demographics, HEXACO personality, Rothbart’s temperament, parenting, friendship quality, school connectedness, and socio-economic status. Subtypes were predicted by low Honesty-Humility in addition to other personality and demographic factors with the exception of physical bullying, which was predicted by environmental factors. Results suggest adolescents adaptively and selectively use bullying to exploit victims and obtain resources, although the subtype used may depend on individual factors bullies possess within Bronfenbrenner’s microsystem, instead of the meso- and exo- systems. Anti-bullying efforts should target these factors and reinforce alternative strategies to obtain resources.

Variability in the Insect and Plant Adhesins, Mad1 and Mad2, within the Fungal Genus Metarhizium Suggest Plant Adaptation as an Evolutionary Force

Several species of the insect pathogenic fungus Metarhizium are associated with certain plant types and genome analyses suggested a bifunctional lifestyle; as an insect pathogen and as a plant symbiont. Here we wanted to explore whether there was more variation in genes devoted to plant association (Mad2) or to insect association (Mad1) overall in the genus Metarhizium. Greater divergence within the genus Metarhizium in one of these genes may provide evidence for whether host insect or plant is a driving force in adaptation and evolution in the genus Metarhizium. We compared differences in variation in the insect adhesin gene, Mad1, which enables attachment to insect cuticle, and the plant adhesin gene, Mad2, which enables attachment to plants. Overall variation for the Mad1 promoter region (7.1%), Mad1 open reading frame (6.7%), and Mad2 open reading frame (7.4%) were similar, while it was higher in the Mad2 promoter region (9.9%). Analysis of the transcriptional elements within the Mad2 promoter region revealed variable STRE, PDS, degenerative TATA box, and TATA box-like regions, while this level of variation was not found for Mad1. Sequences were also phylogenetically compared to EF-1a, which is used for species identification, in 14 isolates representing 7 different species in the genus Metarhizium. Phylogenetic analysis demonstrated that the Mad2 phylogeny is more congruent with 59 EF-1a than Mad1. This would suggest that Mad2 has diverged among Metarhizium lineages, contributing to clade- and species-specific variation, while it appears that Mad1 has been largely conserved. While other abiotic and biotic factors cannot be excluded in contributing to divergence, these results suggest that plant relationships, rather than insect host, have been a major driving factor in the divergence of the genus Metarhizium.

Characterizing Dynamic Optimization Benchmarks for the Comparison of Multi-Modal Tracking Algorithms

Population-based metaheuristics, such as particle swarm optimization (PSO), have been employed to solve many real-world optimization problems. Although it is of- ten sufficient to find a single solution to these problems, there does exist those cases where identifying multiple, diverse solutions can be beneficial or even required. Some of these problems are further complicated by a change in their objective function over time. This type of optimization is referred to as dynamic, multi-modal optimization. Algorithms which exploit multiple optima in a search space are identified as niching algorithms. Although numerous dynamic, niching algorithms have been developed, their performance is often measured solely on their ability to find a single, global optimum. Furthermore, the comparisons often use synthetic benchmarks whose landscape characteristics are generally limited and unknown. This thesis provides a landscape analysis of the dynamic benchmark functions commonly developed for multi-modal optimization. The benchmark analysis results reveal that the mechanisms responsible for dynamism in the current dynamic bench- marks do not significantly affect landscape features, thus suggesting a lack of representation for problems whose landscape features vary over time. This analysis is used in a comparison of current niching algorithms to identify the effects that specific landscape features have on niching performance. Two performance metrics are proposed to measure both the scalability and accuracy of the niching algorithms. The algorithm comparison results demonstrate the algorithms best suited for a variety of dynamic environments. This comparison also examines each of the algorithms in terms of their niching behaviours and analyzing the range and trade-off between scalability and accuracy when tuning the algorithms respective parameters. These results contribute to the understanding of current niching techniques as well as the problem features that ultimately dictate their success.

Psychopathy and the Ability to Detect Cheaters: An Evolutionary Perspective

This research examined psychopathy as an evolutionary adaptation that involves cheating and deception. I theorized that this strategy should be associated with certain abilities. This research examined the association between psychopathic traits and the ability to detect cheaters, altruism, deception, and psychopathic traits. Results indicated that psychopathic traits were not significantly associated with the ability to detect cheaters or altruism. Results indicated that high Factor 1 psychopathy scores, and low Factor 2 psychopathy scores, were indicative of higher ratings of deception when viewing deceptive videos. Conversely, when viewing truthful videos, Factor 1 was a significant predictor of higher ratings of deception. Finally, our results indicated that total psychopathy scores were associated the ability to identify psychopathic traits in others. Taken together the results provide mixed support for the evolutionary perspective of psychopathy.

Properties and Algorithms of the KCube Interconnection Networks

The KCube interconnection network was first introduced in 2010 in order to exploit the good characteristics of two well-known interconnection networks, the hypercube and the Kautz graph. KCube links up multiple processors in a communication network with high density for a fixed degree. Since the KCube network is newly proposed, much study is required to demonstrate its potential properties and algorithms that can be designed to solve parallel computation problems. In this thesis we introduce a new methodology to construct the KCube graph. Also, with regard to this new approach, we will prove its Hamiltonicity in the general KC(m; k). Moreover, we will find its connectivity followed by an optimal broadcasting scheme in which a source node containing a message is to communicate it with all other processors. In addition to KCube networks, we have studied a version of the routing problem in the traditional hypercube, investigating this problem: whether there exists a shortest path in a Qn between two nodes 0n and 1n, when the network is experiencing failed components. We first conditionally discuss this problem when there is a constraint on the number of faulty nodes, and subsequently introduce an algorithm to tackle the problem without restrictions on the number of nodes.

A Scalability Study and New Algorithms for Large-Scale Many-Objective Optimization

Many real-world optimization problems contain multiple (often conflicting) goals to be optimized concurrently, commonly referred to as multi-objective problems (MOPs). Over the past few decades, a plethora of multi-objective algorithms have been proposed, often tested on MOPs possessing two or three objectives. Unfortunately, when tasked with solving MOPs with four or more objectives, referred to as many-objective problems (MaOPs), a large majority of optimizers experience significant performance degradation. The downfall of these optimizers is that simultaneously maintaining a well-spread set of solutions along with appropriate selection pressure to converge becomes difficult as the number of objectives increase. This difficulty is further compounded for large-scale MaOPs, i.e., MaOPs possessing large amounts of decision variables. In this thesis, we explore the challenges of many-objective optimization and propose three new promising algorithms designed to efficiently solve MaOPs. Experimental results demonstrate the proposed optimizers to perform very well, often outperforming state-of-the-art many-objective algorithms.

Models, algorithms, and heuristics for multiobjetive commercial territory design.

Tesis (Doctor en Ingeniería de Sistemas) UANL, 2010.