Evolutionary Algorithms and Computational Methods for Derivatives Pricing

This work aims to provide novel computational solutions to the problem of derivative pricing. To achieve this, a novel hybrid evolutionary algorithm (EA) based on particle swarm optimisation (PSO) and differential evolution (DE) is introduced and applied, along with various other state-of-the-art variants of PSO and DE, to the problem of calibrating the Heston stochastic volatility model. It is found that state-of-the-art DEs provide excellent calibration performance, and that previous use of rudimentary DEs in the literature undervalued the use of these methods. The use of neural networks with EAs for approximating the solution to derivatives pricing models is next investigated. A set of neural networks are trained from Monte Carlo (MC) simulation data to approximate the closed form solution for European, Asian and American style options. The results are comparable to MC pricing, but with offline evaluation of the price using the neural networks being orders of magnitudes faster and computationally more efficient. Finally, the use of custom hardware for numerical pricing of derivatives is introduced. The solver presented here provides an energy efficient data-flow implementation for pricing derivatives, which has the potential to be incorporated into larger high-speed/low energy trading systems

Optimization Algorithms for Computational Systems Biology

Computational systems biology aims at integrating biology and computational methods to gain a better understating of biological phenomena. It often requires the assistance of global optimization to adequately tune its tools. This review presents three powerful methodologies for global optimization that fit the requirements of most of the computational systems biology applications, such as model tuning and biomarker identification. We include the multi-start approach for least squares methods, mostly applied for fitting experimental data. We illustrate Markov Chain Monte Carlo methods, which are stochastic techniques here applied for fitting experimental data when a model involves stochastic equations or simulations. Finally, we present Genetic Algorithms, heuristic nature-inspired methods that are applied in a broad range of optimization applications, including the ones in systems biology

Optimization of nonlinear function with planar regions using supernova

Nowadays, optimization is begun to be use in different fields, e.g. preference algorithms. These new challenges need a robustness meta heuristics to solve them. Supernova meta heuristic that emules the descent behavior of the gradients and share the same weakness of them. They get stuck planar regions and hardly find the needle minimum. The main objective of this works is to improve the performance of the original version of supernova for the problematic topologies mention above. First, a review of how to these problems are solved in the literature is presented. Second, A criterion to determine planar regions is described . Third, a strategy to choose the parameters agree with the topology of the function is implemented. Supernova 2.0 was tested using the set of benchmarks functions proposed in CEC2013. The new version is significantly better than the original version, no significantly better than SPSO2011 and significantly inferior with SADE. Although, the results are applied to Supernova, most of the strategies can be applied to other methods.Doctorad

Historical divergence and gene flow: Coalescent analyses of mitochondrial, autosomal and sex-linked loci in passerina buntings

Quantifying the role of gene flow during the divergence of closely related species is crucial to understanding the process of speciation. We collected DNA sequence data from 20 loci (one mitochondrial, 13 autosomal, and six sex-linked) for population samples of Lazuli Buntings (Passerina amoena) and Indigo Buntings (Passerina cyanea) (Aves: Cardinalidae) to test explicitly between a strict allopatric speciation model and a model in which divergence occurred despite postdivergence gene flow. Likelihood ratio tests of coalescent-based population genetic parameter estimates indicated a strong signal of postdivergence gene flow and a strict allopatric speciation model was rejected. Analyses of partitioned datasets (mitochondrial, autosomal, and sex-linked) suggest the overall gene flow patterns are driven primarily by autosomal gene flow, as there is no evidence of mitochondrial gene flow and we were unable to reject an allopatric speciation model for the sex-linked data. This pattern is consistent with either a parapatric divergence model or repeated periods of allopatry with gene flow occurring via secondary contact. These results are consistent with the low fitness of female avian hybrids under Haldane\u27s rule and demonstrate that sex-linked loci likely are important in the initial generation of reproductive isolation, not just its maintenance. © 2010 The Author(s). Journal compilation © 2010 The Society for the Study of Evolution

Seeking multiple solutions:an updated survey on niching methods and their applications

Multi-Modal Optimization (MMO) aiming to locate multiple optimal (or near-optimal) solutions in a single simulation run has practical relevance to problem solving across many fields. Population-based meta-heuristics have been shown particularly effective in solving MMO problems, if equipped with specificallydesigned diversity-preserving mechanisms, commonly known as niching methods. This paper provides an updated survey on niching methods. The paper first revisits the fundamental concepts about niching and its most representative schemes, then reviews the most recent development of niching methods, including novel and hybrid methods, performance measures, and benchmarks for their assessment. Furthermore, the paper surveys previous attempts at leveraging the capabilities of niching to facilitate various optimization tasks (e.g., multi-objective and dynamic optimization) and machine learning tasks (e.g., clustering, feature selection, and learning ensembles). A list of successful applications of niching methods to real-world problems is presented to demonstrate the capabilities of niching methods in providing solutions that are difficult for other optimization methods to offer. The significant practical value of niching methods is clearly exemplified through these applications. Finally, the paper poses challenges and research questions on niching that are yet to be appropriately addressed. Providing answers to these questions is crucial before we can bring more fruitful benefits of niching to real-world problem solving

Probabilistic structural mechanics research for parallel processing computers

Aerospace structures and spacecraft are a complex assemblage of structural components that are subjected to a variety of complex, cyclic, and transient loading conditions. Significant modeling uncertainties are present in these structures, in addition to the inherent randomness of material properties and loads. To properly account for these uncertainties in evaluating and assessing the reliability of these components and structures, probabilistic structural mechanics (PSM) procedures must be used. Much research has focused on basic theory development and the development of approximate analytic solution methods in random vibrations and structural reliability. Practical application of PSM methods was hampered by their computationally intense nature. Solution of PSM problems requires repeated analyses of structures that are often large, and exhibit nonlinear and/or dynamic response behavior. These methods are all inherently parallel and ideally suited to implementation on parallel processing computers. New hardware architectures and innovative control software and solution methodologies are needed to make solution of large scale PSM problems practical

Agent-based modeling: a systematic assessment of use cases and requirements for enhancing pharmaceutical research and development productivity.

A crisis continues to brew within the pharmaceutical research and development (R&D) enterprise: productivity continues declining as costs rise, despite ongoing, often dramatic scientific and technical advances. To reverse this trend, we offer various suggestions for both the expansion and broader adoption of modeling and simulation (M&S) methods. We suggest strategies and scenarios intended to enable new M&S use cases that directly engage R&D knowledge generation and build actionable mechanistic insight, thereby opening the door to enhanced productivity. What M&S requirements must be satisfied to access and open the door, and begin reversing the productivity decline? Can current methods and tools fulfill the requirements, or are new methods necessary? We draw on the relevant, recent literature to provide and explore answers. In so doing, we identify essential, key roles for agent-based and other methods. We assemble a list of requirements necessary for M&S to meet the diverse needs distilled from a collection of research, review, and opinion articles. We argue that to realize its full potential, M&S should be actualized within a larger information technology framework--a dynamic knowledge repository--wherein models of various types execute, evolve, and increase in accuracy over time. We offer some details of the issues that must be addressed for such a repository to accrue the capabilities needed to reverse the productivity decline