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Using Multivariate Statistics

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Grey Wolf Optimizer

Algorithms are important tools for solving problems computationally. All computation involves algorithms, and the efficiency of an algorithm largely determines its usefulness. This chapter provides an overview of the fundamentals of algorithms and their links to self-organization, exploration, and exploitation. A brief history of recent nature-inspired algorithms for optimization is outlined in this chapter.

Introduction to Algorithms

Foreword: New theoretical insights and practical applications of bio-inspired computation approaches

Deep learning as a data modeling tool is hard to be understood of how its predicted result came about from its inner working. It is generally known as a black box and is not interpretable. Often in medical applications, physicians need to understand why a model predicts a result. On the other hand, BN is a probabilistic graph with nodes representing the variables, and the arcs present the conditional dependences between the variables. In this article, a white learning framework is proposed, which advocates three levels of fusing the black-box deep learning and white box BN, that offers both predictive power and interpretability. A case of breast cancer classification is conducted in an experiment. From the results, it is observed that white learning, which combines black-box and white-box machine learning, has an edge in performance over individually BN alone or deep learning alone. The white learning framework has the benefits of interpretability and high predictive power, making it suitable for critical decision-making task where a reliable prediction is as important as knowing how the outcome is predicted. The predicted output, which is generated from white learning, can be traced back via the conditional probability at each node. It is, hence, anticipated that in the future, especially for medical domain, white learning, which has the benefits of both black -box and white-box learning, would be highly valued and raised in popularity.

White Learning: A White-Box Data Fusion Machine Learning Framework for Extreme and Fast Automated Cancer Diagnosis

Catalytic reactions with inhibition and cooperativity are common in chemical engineering, processing of engineering materials and biological systems. A theoretical model for competitive and cooperative activity in enzyme reactions is formulated in terms of a pair of coupled nonlinear reaction–diffusion equations. A new stochastic cellular automaton is then constructed to simulate this nonlinear system. Numerical simulations show stable two- and three-dimensional Turing pattern formation, and complex patterns have the interesting feature of self-organized criticality.

http://iopscience.iop.org/article/10.1088/0965-0393/11/3/305/pdf

Turing pattern formation of catalytic reaction–diffusion systems in engineering applications

Combinatorial optimization problems, specially those that are NP-hard, are increasingly being dealt with by stochastic, metaheuristic approaches. Most recently developed metaheuristics are nature-inspired and they are often inspired by some special characteristics in evolution, ecological or biological systems. This chapter discusses how to go from a biological phenomenon such as the aggressive reproduction strategy of cuckoos to solve tough problems in the combinatorial search space. Key features and steps are highlighted, together with the discussions of further research topics.

Cuckoo Search: From Cuckoo Reproduction Strategy to Combinatorial Optimization

In almost all applications in engineering and industry, we are always trying to optimize something -whether to minimize the cost and energy consumption, or to maximize the profit, output, performance and efficiency (Yang, 2010b; Yang & Koziel, 2011). The optimal use of available resources of any sort requires a paradigm shift in scientific thinking, this is because most realworld applications have far more complicated factors and parameters to affect how the system behaves. Optimization algorithms are the tools and techniques of achieving the optimality of the problem of interest. This search for optimality is complicated further by the fact that uncertainty almost always presents in the real-world systems. Therefore, we seek not only the optimal design but also robust design in engineering and industry. Optimal design solutions, which are not robust enough, are not practical in reality. Suboptimal solutions or good robust solutions are often the choice in such cases because they are more robust and less sensitive to the uncertainty in the material properties in the real systems. Optimization problems can be formulated in many ways. For example, the commonly used method of least-squares is a special case of maximum-likelihood formulations. By far the most widely formulation is to write a nonlinear optimization problem as

Xin-She Yang

Papers

Foreword: New theoretical insights and practical applications of bio-inspired computation approaches

White Learning: A White-Box Data Fusion Machine Learning Framework for Extreme and Fast Automated Cancer Diagnosis

Turing pattern formation of catalytic reaction–diffusion systems in engineering applications

Cuckoo Search: From Cuckoo Reproduction Strategy to Combinatorial Optimization

Cuckoo Search for Optimization and Computational Intelligence