Network reciprocity describes the emergence of cooperative behaviour where interactions are constrained by incomplete network connectivity. It has been widely studied as an enabling mechanism for the emergence of cooperation and may be of particular interest in explaining cooperative behaviours amongst unrelated individuals or in organisms of lower cognitive abilities. Research in this area has been galvanised by the finding that heterogeneous topology promotes cooperation. Consequently there has been a strong focus on scale-free networks; however, such networks typically presuppose formative mechanisms based on preferential attachment, a process which has no general explanation. This assumption may give rise to models of cooperation that implicitly encode capabilities only generally found in more complex forms of life, thus constraining their relevance with regards to the real world. By considering the connectivity of populations to be dynamic, rather than fixed, cooperation can exist at lower levels of heterogeneity. This thesis demonstrates that a model of network fluctuation, based on random rather than preferential growth, supports cooperative behaviour in simulated social networks of only moderate heterogeneity, thus overcoming difficulties associated with explanations based on scale-free networks. In addition to illustrating the emergence and persistence of cooperation in existing networks, we also demonstrate how cooperation may evolve in networks during their growth. In particular our model supports the emergence of cooperation in populations where it is originally absent. The combined impact of our findings increases the generality of reciprocity as an explanation for cooperation in networks.

This thesis analyzes the changing design and planning strategies in the contemporary urban design area. The rapid improvements during the 20th century in complexity sciences and computer technologies have directly affected all the branches of design. In architecture, as in urban design, generative models, evolutionary design attitudes and computer based simulation tools have taken a significant role during the last few decades. In urban design, emerged in a period starting form the second half of the century, non-determinist, dynamic and self-organized design attitudes depending on naturalistic models have emerged as an alternative to determinist, static and reductionist approaches based on linear solutions. In this study, it is aimed to define and evaluate these emerging contemporary approaches with respect to their antecedents and precedents. The study also searches for the conceptual and technical developments and background which support this process. With an analysis of case studies, the paradigm shift is examined in practice. The study intends to clarify whether contemporary urban design approaches, especially naturalistic models could be an alternative to deterministic stances.

Model-based black-box optimization is a topic that has been intensively studied both in academia and industry. Especially real-world optimization tasks are often characterized by expensive or time-demanding objective functions for which statistical models can save resources or speed-up the optimization. Each of three parts of the thesis concerns one such model: first, copulas are used instead of a graphical model in estimation of distribution algorithms, second, RBF networks serve as surrogate models in mixed-variable genetic algorithms, and third, Gaussian processes are employed in Bayesian optimization algorithms as a sampling model and in the Covariance matrix adaptation Evolutionary strategy (CMA-ES) as a surrogate model. The last combination, described in the core part of the thesis, resulted in the Doubly trained surrogate CMA-ES (DTS-CMA-ES). This algorithm uses the uncertainty prediction of a Gaussian process for selecting only a part of the CMA-ES population for evaluation with the expensive objective function while the mean prediction is used for the rest. The DTS-CMA-ES improves upon the state-of-the-art surrogate continuous optimizers in several benchmark tests.

The master's thesis provides a comprehensive overview of resampling methods for testing the correctness topology of the phylogenetic trees which estimate the process of phylogeny on the bases of biological sequences similarity. We focused on the possibility of errors creation in this estimate and the possibility of their removal and detection. These methods were implemented in Matlab for Bootstrapping, jackknifing, OTU jackknifing and PTP test (Permutation tail probability). The work aims to test their applicability to various biological sequences and also to assess the impact of the choice of input analysis parameters on the results of these statistical tests.

Sympatric speciation has received much attention both empirically and theoretically. However, the contribution of sympatric speciation to biodiversity remains unclear. One piece missing from the speciation puzzle is the plausibility of sympatric ecological divergence of species through adaptation in polygenic traits. I consider an environment consisting of two niches, where one value of the trait is advantageous in only one niche, and vice versa. The selection regime is described by a trade-off in viabilities between the niches. These polygenic traits can, and often do, involve epistatic interactions among and between loci, so that the contribution of the alleles to viability deviates from additivity. Epistasis then also affects the curvature of the trade-offs: predominant less-than-additive epistasis turns the curve towards concavity and predominant more-than-additive towards convexity. The curvature of the trade-off plays a crucial role in the evolution of populations. With a convex trade- off, extreme values of the trait are favored and the population tends to diverge, but relatively stringent symmetry in strength of selection within the niches and the niche proportions is necessary to maintain polymorphism. In this study I use two and three- locus haploid versions of Levene's model to...