The nesting biology of Ceratina (Hymenoptera: Apidae) in the Niagara Region : new species, nest site selection and parasitism

One of the most common bee genera in the Niagara Region, the genus Ceratina (Hymenoptera: Apidae) is composed of four species, C. dupla, C. calcarata, the very rare C. strenua, and a previously unknown species provisionally named C. near dupla. The primary goal of this thesis was to investigate how these closely related species coexist with one another in the Niagara ~ee community. The first necessary step was to describe and compare the nesting biologies and life histories of the three most common species, C. dupla, C. calcarata and the new C. near dupla, which was conducted in 2008 via nest collections and pan trapping. Ceratina dupla and C. calcarata were common, each comprising 49% of the population, while C. near dupla was rare, comprising only 2% of the population. Ceratina dupla and C. near dupla both nested more commonly in teasel (Dipsacus sp.) in the sun, occasionally in raspberry (Rubus sp.) in the shade, and never in shady sumac (Rhus sp.), while C. calcarata nested most commonly in raspberry and sumac (shaded) and occasionally in teasel (sunny). Ceratina near dupla differed from both C. dupla and C. calcarata in that it appeared to be partially bivoltine, with some females founding nests very early and then again very late in the season. To examine the interactions and possible competition for nests that may be taking place between C. dupla and C. calcarata, a nest choice experiment was conducted in 2009. This experiment allowed both species to choose among twigs from all three substrates in the sun and in the shade. I then compared the results from 2008 (where bees chose from what was available), to where they nested when given all options (2009 experiment). Both C. dupla and C. calcarata had the same preferences for microhabitat and nest substrate in 2009, that being raspberry and sumac twigs in the sun. As that microhabitat and nest substrate combination is extremely rare in nature, both species must make a choice. In nature Ceratina dupla nests more often in the preferred microhabitat (sun), while C. calcarata nests in the preferred substrate (raspberry). Nesting in the shade also leads to smaller clutch sizes, higher parasitism and lower numbers of live brood in C. calcarata, suggesting that C. dupla may be outcompeting C. calcarata for the sunny nesting sites. The development and host preferences of Ceratina parasitoids were also examined. Ceratina species in Niagara were parasitized by no less than eight species of arthropod. Six of these were wasps from the superfamily Chalcidoidea (Hymenoptera), one was a wasp from the family Ichneumonidae (Hymenoptera) and one was a physogastric mite from the family Pyemotidae (Acari). Parasites shared a wide range of developmental strategies, from ichneumonid larvae that needed to consume multiple Ceratina immatures to complete development, to the species from the Eulophidae (Baryscapus) and Encyrtidae (Coelopencyrtus), in which multiple individuals completed development inside a single Ceratina host. Biological data on parasitoids is scarce in the scientific literature, and this Chapter documents these interactions for future research.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and deterministic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel metaheuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS metaheuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and determinis- tic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel meta–heuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS meta–heuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

GA approach for finding Rough Set decision rules based on bireducts

Feature selection plays an important role in knowledge discovery and data mining nowadays. In traditional rough set theory, feature selection using reduct - the minimal discerning set of attributes - is an important area. Nevertheless, the original definition of a reduct is restrictive, so in one of the previous research it was proposed to take into account not only the horizontal reduction of information by feature selection, but also a vertical reduction considering suitable subsets of the original set of objects. Following the work mentioned above, a new approach to generate bireducts using a multi--objective genetic algorithm was proposed. Although the genetic algorithms were used to calculate reduct in some previous works, we did not find any work where genetic algorithms were adopted to calculate bireducts. Compared to the works done before in this area, the proposed method has less randomness in generating bireducts. The genetic algorithm system estimated a quality of each bireduct by values of two objective functions as evolution progresses, so consequently a set of bireducts with optimized values of these objectives was obtained. Different fitness evaluation methods and genetic operators, such as crossover and mutation, were applied and the prediction accuracies were compared. Five datasets were used to test the proposed method and two datasets were used to perform a comparison study. Statistical analysis using the one-way ANOVA test was performed to determine the significant difference between the results. The experiment showed that the proposed method was able to reduce the number of bireducts necessary in order to receive a good prediction accuracy. Also, the influence of different genetic operators and fitness evaluation strategies on the prediction accuracy was analyzed. It was shown that the prediction accuracies of the proposed method are comparable with the best results in machine learning literature, and some of them outperformed it.

Core Retionalizability in Two-Agent Exchange Economies.

We provide a characterization of selection correspondences in two-person exchange economies that can be core rationalized in the sense that there exists a preference profile with some standard properties that generates the observed choices as the set of core elements of the economy for any given initial endowment vector. The approach followed in this paper deviates from the standard rational choice model in that a rationalization in terms of a profile of individual orderings rather than in terms of a single individual or social preference relation is analyzed.

Exact Nonparametric Two-Sample Homogeneity Tests for Possibly Discrete Distributions.

In this paper, we study several tests for the equality of two unknown distributions. Two are based on empirical distribution functions, three others on nonparametric probability density estimates, and the last ones on differences between sample moments. We suggest controlling the size of such tests (under nonparametric assumptions) by using permutational versions of the tests jointly with the method of Monte Carlo tests properly adjusted to deal with discrete distributions. We also propose a combined test procedure, whose level is again perfectly controlled through the Monte Carlo test technique and has better power properties than the individual tests that are combined. Finally, in a simulation experiment, we show that the technique suggested provides perfect control of test size and that the new tests proposed can yield sizeable power improvements.

The Transformation Problem : an Aleternative Solution with an Identical Aggregate Profit Rate in the Labor value and the Monetary Space.

The aim of this paper is to demonstrate that, even if Marx's solution to the transformation problem can be modified, his basic conclusions remain valid. the proposed alternative solution which is presented hare is based on the constraint of a common general profit rate in both spaces and a money wage level which will be determined simultaneously with prices.

The Wage Rate and the Profit Rate in the Price of Production Equation: a New Solution to an Old Problem

The aim of this paper is to demonstrate that, even if Marx's solution to the transformation problem can be modified, his basic concusions remain valid.