Demographic and genetic attributes of dispersing and resident individuals of an enclosed Microtus pennsylvanicus population

A dispersal polymorphism may exist in emigrants from cyclic populations of Microtus '~nnsylvanicus biasing trap-revealed movements of unenclosed animals in favour of sedentary or colonizing individuals. The dispersal tendency of emigrants from an enclosed population was investigated by releasing animals via tubes into one of two adjacent enclosures, one vacant and one inhabited. Individuals from the enclosed population were monitored for age, sex, weight and electrophoretically detectable serum transferrin genotype in an intensive live-trapping program. In 1973 the minimum number alive in the introduced enclosed study population reached approximately l67/ha when breeding stopped in October. In 1974 intensive breeding increased the population density to 333/ha by mid-July when a long decline in numbers and breeding intensity began without an intervening plateau. An adjacent unenclosed area had a much lower density and longer breeding season in 1974. The growth rate of young males in the enclosed population tended to be lowest during the decline period in 1974. Survival of the enclosed population was high throughout but was lowest during the decline phase in both sexes, especially males. Low transferrin heterozygote survival during the decline coincided with a significant heterozygote deficiency in females whereas in males genotype frequencies did not depart from Hardy-Weinberg equilibrium values throughout th.e study. Twenty-nine suitable ani.mals were released during the decline in five periods from July to November 1974. The proportions of males and transferrin heterozygotes in the released graun were generally greater than in the source population~ In the test enclosures 21% of the released animals continued their movement through the vacant area while 41% (no significant difference) moved through the inhabited enclosure. In the vacant test area, females had a greater tendency than males to continue dispersal whereas no difference was noted in the inhabited area. Low frequency of captures in the tubes, predator disturbances and cold weather forced the termination of the study. The role of dispersal as a population regulating mechanism was further substantiated. The genetic differences between emigrant and resident animals lend support to Howard's hypothesis that a genetic polymorphism influences the tendency to disperse. Support is also given to Myers' and Krebs' contention that among dispersers an additional density dependent polymorphism influences the distance dispersed.

Using genetic algorithms for the single allocation hub location problem

Hub location problem is an NP-hard problem that frequently arises in the design of transportation and distribution systems, postal delivery networks, and airline passenger flow. This work focuses on the Single Allocation Hub Location Problem (SAHLP). Genetic Algorithms (GAs) for the capacitated and uncapacitated variants of the SAHLP based on new chromosome representations and crossover operators are explored. The GAs is tested on two well-known sets of real-world problems with up to 200 nodes. The obtained results are very promising. For most of the test problems the GA obtains improved or best-known solutions and the computational time remains low. The proposed GAs can easily be extended to other variants of location problems arising in network design planning in transportation systems.

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.

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.