Mining Explicit and Implicit Relationships in Data Using Symbolic Regression

Identification of implicit and explicit relations within observed data is a generic problem commonly encountered in several domains including science, engineering, finance, and more. It forms the core component of data analytics, a process of discovering useful information from data sets that are potentially huge and otherwise incomprehensible. In industries, such information is often instrumental for profitable decision making, whereas in science and engineering it is used to build empirical models, propose new or verify existing theories and explain natural phenomena. In recent times, digital and internet based technologies have proliferated, making it viable to generate and collect large amount of data at low cost. This inturn has resulted in an ever growing need for methods to analyse and draw interpretations from such data quickly and reliably. With this overarching goal, this thesis attempts to make contributions towards developing accurate and efficient methods for discovering such relations through evolutionary search, a method commonly referred to as Symbolic Regression (SR). A data set of input variables x and a corresponding observed response y is given. The aim is to find an explicit function y = f (x) or an implicit function f (x, y) = 0, which represents the data set. While seemingly simple, the problem is challenging for several reasons. Some of the conventional regression methods try to “guess” a functional form such as linear/quadratic/polynomial, and attempt to do a curve-fitting of the data to the equation, which may limit the possibility of discovering more complex relations, if they exist. On the other hand, there are meta-modelling techniques such as response surface method, Kriging, etc., that model the given data accurately, but provide a “black-box” predictor instead of an expression. Such approximations convey little or no insights about how the variables and responses are dependent on each other, or their relative contribution to the output. SR attempts to alleviate the above two extremes by providing a structure which evolves mathematical expressions instead of assuming them. Thus, it is flexible enough to represent the data, but at the same time provides useful insights instead of a black-box predictor. SR can be categorized as part of Explainable Artificial Intelligence and can contribute to Trustworthy Artificial Intelligence. The works proposed in this thesis aims to integrate the concept of “semantics” deeper into Genetic Programming (GP) and Evolutionary Feature Synthesis, which are the two algorithms usually employed for conducting SR. The semantics will be integrated into well-known components of the algorithms such as compactness, diversity, recombination, constant optimization, etc. The main contribution of this thesis is the proposal of two novel operators to generate expressions based on Linear Programming and Mixed Integer Programming with the aim of controlling the length of the discovered expressions without compromising on the accuracy. In the experiments, these operators are proven to be able to discover expressions with better accuracy and interpretability on many explicit and implicit benchmarks. Moreover, some applications of SR on real-world data sets are shown to demonstrate the practicality of the proposed approaches. Besides, in related to practical problems, how GP can be applied to effectively solve the Resource Constrained Scheduling Problems is also presented

CAD interface and framework for curve optimisation applications

Computer Aided Design is currently expanding its boundaries to include more design features in its processes. Design is identified as an iterative process converging to solutions satisfying a set of constraints. Its close relation with optimisation indicate that there is strong potential for the integration of optimisation and CAD. The problem addressed in this thesis lies in interfacing the geometric representation of design with other non-geometric aspects. The example of free-form curve modelling is taken to investigate such relationships. Assumptions are made that Optimisation is powered by Evolutionary Computing algorithms like Genetic Algorithms (GA). The geometric definition of curves is commonly supported by NURBS, whose construction constraints are defined locally at the data points. Here the NURBS formulation is used with GA in an attempt to provide complementary handles on the curves shape other than the usual data point coordinates and control points weights. Differential properties are used for optimising NURBS, Hermite interpolation allows for the definition of higher order constraints (tangent, normal, bi-normal) at data points. The assignment of parameter values at the data points, known as parameterisation also provides control of the curve’s shape. Curve optimisation is also performed at the geometric modelling level. Old mathematical theorems established by Frénet and further developed by other mathematicians provide means of defining a curve’s shape with it’s intrinsic equations. Such representation is possible by using Function Representation (F-rep) algebra available in the ACIS software. Frep allows more generic and exact means of interfacing with the curve’s geometry and new functionality for curve inspection and optimisation are proposed in this thesis. The integration of optimisation findings and CAD are documented in the definition of a framework. The framework architecture proposed reconstructs a new CAD environment from separate elements bolted together in a generic Application Programming Interface (API) named “Oli interface”. Functionality created to interface optimisation and CAD makes a requirement list of the work that both sides should undertake to achieve design optimisation in the CAD environment.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

The 1989 Goddard Conference on Space Applications of Artificial Intelligence

The following topics are addressed: mission operations support; planning and scheduling; fault isolation/diagnosis; image processing and machine vision; data management; and modeling and simulation

The 1992 Goddard Conference on Space Applications of Artificial Intelligence

The purpose of this conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The papers fall into the following areas: planning and scheduling, control, fault monitoring/diagnosis and recovery, information management, tools, neural networks, and miscellaneous applications

First Annual Workshop on Space Operations Automation and Robotics (SOAR 87)

Several topics relative to automation and robotics technology are discussed. Automation of checkout, ground support, and logistics; automated software development; man-machine interfaces; neural networks; systems engineering and distributed/parallel processing architectures; and artificial intelligence/expert systems are among the topics covered