Likelihood-based inference of B-cell clonal families

The human immune system depends on a highly diverse collection of antibody-making B cells. B cell receptor sequence diversity is generated by a random recombination process called "rearrangement" forming progenitor B cells, then a Darwinian process of lineage diversification and selection called "affinity maturation." The resulting receptors can be sequenced in high throughput for research and diagnostics. Such a collection of sequences contains a mixture of various lineages, each of which may be quite numerous, or may consist of only a single member. As a step to understanding the process and result of this diversification, one may wish to reconstruct lineage membership, i.e. to cluster sampled sequences according to which came from the same rearrangement events. We call this clustering problem "clonal family inference." In this paper we describe and validate a likelihood-based framework for clonal family inference based on a multi-hidden Markov Model (multi-HMM) framework for B cell receptor sequences. We describe an agglomerative algorithm to find a maximum likelihood clustering, two approximate algorithms with various trade-offs of speed versus accuracy, and a third, fast algorithm for finding specific lineages. We show that under simulation these algorithms greatly improve upon existing clonal family inference methods, and that they also give significantly different clusters than previous methods when applied to two real data sets

Review of Metaheuristics and Generalized Evolutionary Walk Algorithm

Metaheuristic algorithms are often nature-inspired, and they are becoming very powerful in solving global optimization problems. More than a dozen of major metaheuristic algorithms have been developed over the last three decades, and there exist even more variants and hybrid of metaheuristics. This paper intends to provide an overview of nature-inspired metaheuristic algorithms, from a brief history to their applications. We try to analyze the main components of these algorithms and how and why they works. Then, we intend to provide a unified view of metaheuristics by proposing a generalized evolutionary walk algorithm (GEWA). Finally, we discuss some of the important open questions.Comment: 14 page

A Brief Review of Bio-Inspired Algorithms in Computational Perspective - Bio Inspired Algorithms

Computing over the years has evolved from being simplex mathematical processing machine to more sophisticated problem solving entity pushing limits around reasoning and intelligence. Along the way, lots scientists and engineers have closely observed some of the biological processes achieving certain things in a more efficient and simple fashion than traditional computational mechanisms. This has led to development of various techniques and algorithms which try and mimic these biological processes and are categorised under, Bio-Inspired Computing

Nature inspired computational intelligence for financial contagion modelling

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Financial contagion refers to a scenario in which small shocks, which initially affect only a few financial institutions or a particular region of the economy, spread to the rest of the financial sector and other countries whose economies were previously healthy. This resembles the “transmission” of a medical disease. Financial contagion happens both at domestic level and international level. At domestic level, usually the failure of a domestic bank or financial intermediary triggers transmission by defaulting on inter-bank liabilities, selling assets in a fire sale, and undermining confidence in similar banks. An example of this phenomenon is the failure of Lehman Brothers and the subsequent turmoil in the US financial markets. International financial contagion happens in both advanced economies and developing economies, and is the transmission of financial crises across financial markets. Within the current globalise financial system, with large volumes of cash flow and cross-regional operations of large banks and hedge funds, financial contagion usually happens simultaneously among both domestic institutions and across countries. There is no conclusive definition of financial contagion, most research papers study contagion by analyzing the change in the variance-covariance matrix during the period of market turmoil. King and Wadhwani (1990) first test the correlations between the US, UK and Japan, during the US stock market crash of 1987. Boyer (1997) finds significant increases in correlation during financial crises, and reinforces a definition of financial contagion as a correlation changing during the crash period. Forbes and Rigobon (2002) give a definition of financial contagion. In their work, the term interdependence is used as the alternative to contagion. They claim that for the period they study, there is no contagion but only interdependence. Interdependence leads to common price movements during periods both of stability and turmoil. In the past two decades, many studies (e.g. Kaminsky et at., 1998; Kaminsky 1999) have developed early warning systems focused on the origins of financial crises rather than on financial contagion. Further authors (e.g. Forbes and Rigobon, 2002; Caporale et al, 2005), on the other hand, have focused on studying contagion or interdependence. In this thesis, an overall mechanism is proposed that simulates characteristics of propagating crisis through contagion. Within that scope, a new co-evolutionary market model is developed, where some of the technical traders change their behaviour during crisis to transform into herd traders making their decisions based on market sentiment rather than underlying strategies or factors. The thesis focuses on the transformation of market interdependence into contagion and on the contagion effects. The author first build a multi-national platform to allow different type of players to trade implementing their own rules and considering information from the domestic and a foreign market. Traders’ strategies and the performance of the simulated domestic market are trained using historical prices on both markets, and optimizing artificial market’s parameters through immune - particle swarm optimization techniques (I-PSO). The author also introduces a mechanism contributing to the transformation of technical into herd traders. A generalized auto-regressive conditional heteroscedasticity - copula (GARCH-copula) is further applied to calculate the tail dependence between the affected market and the origin of the crisis, and that parameter is used in the fitness function for selecting the best solutions within the evolving population of possible model parameters, and therefore in the optimization criteria for contagion simulation. The overall model is also applied in predictive mode, where the author optimize in the pre-crisis period using data from the domestic market and the crisis-origin foreign market, and predict in the crisis period using data from the foreign market and predicting the affected domestic market

Solving job shop scheduling problem using genetic algorithm with penalty function

This paper presents a genetic algorithm with a penalty function for the job shop scheduling problem. In the context of proposed algorithm, a clonal selection based hyper mutation and a life span extended strategy is designed. During the search process, an adaptive penalty function is designed so that the algorithm can search in both feasible and infeasible regions of the solution space. Simulated experiments were conducted on 23 benchmark instances taken from the OR-library. The results show the effectiveness of the proposed algorithm

Monitoring and Control Framework for Advanced Power Plant Systems Using Artificial Intelligence Techniques

This dissertation presents the design, development, and simulation testing of a monitoring and control framework for dynamic systems using artificial intelligence techniques. A comprehensive monitoring and control system capable of detecting, identifying, evaluating, and accommodating various subsystem failures and upset conditions is presented. The system is developed by synergistically merging concepts inspired from the biological immune system with evolutionary optimization algorithms and adaptive control techniques.;The proposed methodology provides the tools for addressing the complexity and multi-dimensionality of the modern power plants in a comprehensive and integrated manner that classical approaches cannot achieve. Current approaches typically address abnormal condition (AC) detection of isolated subsystems of low complexity, affected by specific AC involving few features with limited identification capability. They do not attempt AC evaluation and mostly rely on control system robustness for accommodation. Addressing the problem of power plant monitoring and control under AC at this level of completeness has not yet been attempted.;Within the proposed framework, a novel algorithm, namely the partition of the universe, was developed for building the artificial immune system self. As compared to the clustering approach, the proposed approach is less computationally intensive and facilitates the use of full-dimensional self for system AC detection, identification, and evaluation. The approach is implemented in conjunction with a modified and improved dendritic cell algorithm. It allows for identifying the failed subsystems without previous training and is extended to address the AC evaluation using a novel approach.;The adaptive control laws are designed to augment the performance and robustness of baseline control laws under normal and abnormal operating conditions. Artificial neural network-based and artificial immune system-based approaches are developed and investigated for an advanced power plant through numerical simulation.;This dissertation also presents the development of an interactive computational environment for the optimization of power plant control system using evolutionary techniques with immunity-inspired enhancements. Several algorithms mimicking mechanisms of the immune system of superior organisms, such as cloning, affinity-based selection, seeding, and vaccination are used. These algorithms are expected to enhance the computational effectiveness, improve convergence, and be more efficient in handling multiple local extrema, through an adequate balance between exploration and exploitation.;The monitoring and control framework formulated in this dissertation applies to a wide range of technical problems. The proposed methodology is demonstrated with promising results using a high validity DynsimRTM model of the acid gas removal unit that is part of the integrated gasification combined cycle power plant available at West Virginia University AVESTAR Center. The obtained results show that the proposed system is an efficient and valuable technique to be applied to a real world application. The implementation of this methodology can potentially have significant impacts on the operational safety of many complex systems

Traveling Salesman Problem

This book is a collection of current research in the application of evolutionary algorithms and other optimal algorithms to solving the TSP problem. It brings together researchers with applications in Artificial Immune Systems, Genetic Algorithms, Neural Networks and Differential Evolution Algorithm. Hybrid systems, like Fuzzy Maps, Chaotic Maps and Parallelized TSP are also presented. Most importantly, this book presents both theoretical as well as practical applications of TSP, which will be a vital tool for researchers and graduate entry students in the field of applied Mathematics, Computing Science and Engineering