AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Data mining in soft computing framework: a survey

The present article provides a survey of the available literature on data mining using soft computing. A categorization has been provided based on the different soft computing tools and their hybridizations used, the data mining function implemented, and the preference criterion selected by the model. The utility of the different soft computing methodologies is highlighted. Generally fuzzy sets are suitable for handling the issues related to understandability of patterns, incomplete/noisy data, mixed media information and human interaction, and can provide approximate solutions faster. Neural networks are nonparametric, robust, and exhibit good learning and generalization capabilities in data-rich environments. Genetic algorithms provide efficient search algorithms to select a model, from mixed media data, based on some preference criterion/objective function. Rough sets are suitable for handling different types of uncertainty in data. Some challenges to data mining and the application of soft computing methodologies are indicated. An extensive bibliography is also included

Finding Nonlinear Relationships in Functional Magnetic Resonance Imaging Data with Genetic Programming

The human brain is a complex, nonlinear dynamic chaotic system that is poorly understood. When faced with these difficult to understand systems, it is common to observe the system and develop models such that the underlying system might be deciphered. When observing neurological activity within the brain with functional magnetic resonance imaging (fMRI), it is common to develop linear models of functional connectivity; however, these models are incapable of describing the nonlinearities we know to exist within the system. A genetic programming (GP) system was developed to perform symbolic regression on recorded fMRI data. Symbolic regression makes fewer assumptions than traditional linear tools and can describe nonlinearities within the system. Although GP is a powerful form of machine learning that has many drawbacks (computational cost, overfitting, stochastic), it may provide new insights into the underlying system being studied. The contents of this thesis are presented in an integrated article format. For all articles, data from the Human Connectome Project were used. In the first article, nonlinear models for 507 subjects performing a motor task were created. These nonlinear models generated by GP contained fewer ROI than what would be found with traditional, linear tools. It was found that the generated nonlinear models would not fit the data as well as the linear models; however, when compared to linear models containing a similar number of ROI, the nonlinear models performed better. Ten subjects performing 7 tasks were studied in article two. After improvements to the GP system, the generated nonlinear models outperformed the linear models in many cases and were never significantly worse than the linear models. Forty subjects performing 7 tasks were studied in article three. Newly generated nonlinear models were applied to unseen data from the same subject performing the same task (intrasubject generalization) and many nonlinear models generalized to unseen data better than the linear models. The nonlinear models were applied to unseen data from other subjects performing the same task (intersubject generalization) and were not capable of generalizing as well as the linear

A Field Guide to Genetic Programming

xiv, 233 p. : il. ; 23 cm.Libro ElectrónicoA Field Guide to Genetic Programming (ISBN 978-1-4092-0073-4) is an introduction to genetic programming (GP). GP is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. Using ideas from natural evolution, GP starts from an ooze of random computer programs, and progressively refines them through processes of mutation and sexual recombination, until solutions emerge. All this without the user having to know or specify the form or structure of solutions in advance. GP has generated a plethora of human-competitive results and applications, including novel scientific discoveries and patentable inventions. The authorsIntroduction -- Representation, initialisation and operators in Tree-based GP -- Getting ready to run genetic programming -- Example genetic programming run -- Alternative initialisations and operators in Tree-based GP -- Modular, grammatical and developmental Tree-based GP -- Linear and graph genetic programming -- Probalistic genetic programming -- Multi-objective genetic programming -- Fast and distributed genetic programming -- GP theory and its applications -- Applications -- Troubleshooting GP -- Conclusions.Contents xi 1 Introduction 1.1 Genetic Programming in a Nutshell 1.2 Getting Started 1.3 Prerequisites 1.4 Overview of this Field Guide I Basics 2 Representation, Initialisation and GP 2.1 Representation 2.2 Initialising the Population 2.3 Selection 2.4 Recombination and Mutation Operators in Tree-based 3 Getting Ready to Run Genetic Programming 19 3.1 Step 1: Terminal Set 19 3.2 Step 2: Function Set 20 3.2.1 Closure 21 3.2.2 Sufficiency 23 3.2.3 Evolving Structures other than Programs 23 3.3 Step 3: Fitness Function 24 3.4 Step 4: GP Parameters 26 3.5 Step 5: Termination and solution designation 27 4 Example Genetic Programming Run 4.1 Preparatory Steps 29 4.2 Step-by-Step Sample Run 31 4.2.1 Initialisation 31 4.2.2 Fitness Evaluation Selection, Crossover and Mutation Termination and Solution Designation Advanced Genetic Programming 5 Alternative Initialisations and Operators in 5.1 Constructing the Initial Population 5.1.1 Uniform Initialisation 5.1.2 Initialisation may Affect Bloat 5.1.3 Seeding 5.2 GP Mutation 5.2.1 Is Mutation Necessary? 5.2.2 Mutation Cookbook 5.3 GP Crossover 5.4 Other Techniques 32 5.5 Tree-based GP 39 6 Modular, Grammatical and Developmental Tree-based GP 47 6.1 Evolving Modular and Hierarchical Structures 47 6.1.1 Automatically Defined Functions 48 6.1.2 Program Architecture and Architecture-Altering 50 6.2 Constraining Structures 51 6.2.1 Enforcing Particular Structures 52 6.2.2 Strongly Typed GP 52 6.2.3 Grammar-based Constraints 53 6.2.4 Constraints and Bias 55 6.3 Developmental Genetic Programming 57 6.4 Strongly Typed Autoconstructive GP with PushGP 59 7 Linear and Graph Genetic Programming 61 7.1 Linear Genetic Programming 61 7.1.1 Motivations 61 7.1.2 Linear GP Representations 62 7.1.3 Linear GP Operators 64 7.2 Graph-Based Genetic Programming 65 7.2.1 Parallel Distributed GP (PDGP) 65 7.2.2 PADO 67 7.2.3 Cartesian GP 67 7.2.4 Evolving Parallel Programs using Indirect Encodings 68 8 Probabilistic Genetic Programming 8.1 Estimation of Distribution Algorithms 69 8.2 Pure EDA GP 71 8.3 Mixing Grammars and Probabilities 74 9 Multi-objective Genetic Programming 75 9.1 Combining Multiple Objectives into a Scalar Fitness Function 75 9.2 Keeping the Objectives Separate 76 9.2.1 Multi-objective Bloat and Complexity Control 77 9.2.2 Other Objectives 78 9.2.3 Non-Pareto Criteria 80 9.3 Multiple Objectives via Dynamic and Staged Fitness Functions 80 9.4 Multi-objective Optimisation via Operator Bias 81 10 Fast and Distributed Genetic Programming 83 10.1 Reducing Fitness Evaluations/Increasing their Effectiveness 83 10.2 Reducing Cost of Fitness with Caches 86 10.3 Parallel and Distributed GP are Not Equivalent 88 10.4 Running GP on Parallel Hardware 89 10.4.1 Master–slave GP 89 10.4.2 GP Running on GPUs 90 10.4.3 GP on FPGAs 92 10.4.4 Sub-machine-code GP 93 10.5 Geographically Distributed GP 93 11 GP Theory and its Applications 97 11.1 Mathematical Models 98 11.2 Search Spaces 99 11.3 Bloat 101 11.3.1 Bloat in Theory 101 11.3.2 Bloat Control in Practice 104 III Practical Genetic Programming 12 Applications 12.1 Where GP has Done Well 12.2 Curve Fitting, Data Modelling and Symbolic Regression 12.3 Human Competitive Results – the Humies 12.4 Image and Signal Processing 12.5 Financial Trading, Time Series, and Economic Modelling 12.6 Industrial Process Control 12.7 Medicine, Biology and Bioinformatics 12.8 GP to Create Searchers and Solvers – Hyper-heuristics xiii 12.9 Entertainment and Computer Games 127 12.10The Arts 127 12.11Compression 128 13 Troubleshooting GP 13.1 Is there a Bug in the Code? 13.2 Can you Trust your Results? 13.3 There are No Silver Bullets 13.4 Small Changes can have Big Effects 13.5 Big Changes can have No Effect 13.6 Study your Populations 13.7 Encourage Diversity 13.8 Embrace Approximation 13.9 Control Bloat 13.10 Checkpoint Results 13.11 Report Well 13.12 Convince your Customers 14 Conclusions Tricks of the Trade A Resources A.1 Key Books A.2 Key Journals A.3 Key International Meetings A.4 GP Implementations A.5 On-Line Resources 145 B TinyGP 151 B.1 Overview of TinyGP 151 B.2 Input Data Files for TinyGP 153 B.3 Source Code 154 B.4 Compiling and Running TinyGP 162 Bibliography 167 Inde

Constructivist Artificial Intelligence With Genetic Programming

Learning is an essential attribute of an intelligent system. A proper understanding of the process of learning in terms of knowledge-acquisition, processing and its effective use has been one of the main goals of artificial intelligence (AI). AI, in order to achieve the desired flexibility, performance levels and wide applicability should explore and exploit a variety of learning techniques and representations. Evolutionary algorithms, in recent years, have emerged as powerful learning methods employing task-independent approaches to problem solving and are potential candidates for implementing adaptive computational models. These algorithms, due to their attractive features such as implicit and explicit parallelism, can also be powerful meta-leaming tools for other learning systems such as connectionist networks. These networks, also known as artificial neural networks, offer a paradigm for learning at an individual level that provide an extremely rich landscape of learning mechanisms which AI should exploit. The research proposed in this thesis investigates the role of genetic programming (GP) in connectionism, a learning paradigm that, despite being extremely powerful has a number of limitations. The thesis, by systematically identifying the reasons for these limitations has argued as to why connectionism should be approached with a new perspective in order to realize its true potentialities. With genetic-based designs the key issue has been the encoding strategy. That is, how to encode a neural network within a genotype so as to achieve an optimum network structure and/ or an efficient learning that can best solve a given problem. This in turn raises a number of key questions such as: 1. Is the representation (that is the genotype) that the algorithms employ sufficient to express and explore the vast space of network architectures and learning mechanisms? 2. Is the representation capable of capturing the concepts of hierarchy and modularity that are vital and so naturally employed by humans in problem-solving? 3. Are some representations better in expressing these? If so, how to exploit the strengths that are inherent to these representations? 4. If the aim is really to automate the design process what strategies should be employed so as to minimize the involvement of a designer in the design loop? 5. Is the methodology or the approach able to overcome at least some of the limitations that are commonly seen in connectionist networks? 6. Most importantly, how effective is the approach in problem-solving? These issues are investigated through a novel approach that combines genetic programming and a self-organizing neural network which provides a framework for the simulations. Through the powerful notions of constructivism and micro-macro dynamics the approach provides a way of exploiting the potential features (such as the hierarchy and modularity) that are inherent to the representation that GP employs. By providing a general definition for learning and by imposing a single potential constraint within the representation the approach demonstrates that genetic programming, if used for construction and optimization, could be extremely creative. The method also combines the bottom-up and top-down strategies that are key to evolve ALife-like systems. A comparison with earlier methods is drawn to identify the merits of the proposed approach. A pattern recognition task is considered for illustration. Simulations suggest that genetic- programming can be a powerful meta-leaming tool for implementing useful network architectures and flexible learning mechanisms for self-organizing neural networks while interacting with a given task environment. It appears that it is possible to extend the novel approach further to other types of networks. Finally the role of flexible learning in implementing adaptive AI systems is discussed. A number of potential applications domain is identified

Learning AI with deepint.net

This keynote will examine the evolution of intelligent computer systems over the last years, underscoring the need for human capital in this field, so that further progress can be made. In this regard, learning about AI through experience is a big challenge, but it is possible thanks to tools such as deepint.net, which enable anyone to develop AI systems; knowledge of programming is no longer necessary

Efficiency and Reliability in Bringing AI into Transport and Smart Cities Solutions

capacity and the low cost of the Cloud have facilitated the development of new, powerful algorithms. The efficiency of these algorithms in Big Data processing, Deep Learning and Convolutional Networks is transforming the way we work and is opening new horizons. Thanks to them, we can now analyse data and obtain unimaginable solutions to today’s problems. Nevertheless, our success is not entirely based on algorithms, it also comes from our ability to follow our “gut” when choosing the best combination of algorithms for an intelligent artefact. Their development involves the use of both connectionist and symbolic systems, that is to say data and knowledge. Moreover, it is necessary to work with both historical and real-time data. It is also important to consider development time, costs and the ability to create systems that will interact with their environment, will connect with the objects that surround them and will manage the data they obtain in a reliable manner. In this keynote, the evolution of intelligent computer systems will be examined, especially that of convolutional networks. The need for human capital will be discussed, as well as the need to follow one’s “gut instinct” in problem-solving. Furthermore, the importance of IoT and Blockchain in the development of intelligent systems will be analysed and it will be shown how tools like "Deep Intelligence" make it possible to create computer systems efficiently and effectively. "Smart" infrastructures need to incorporate all added-value resources so they can offer useful services to the society, while reducing costs, ensuring reliability and improving the quality of life of the citizens. The combination of AI with IoT and with blockchain offers a world of possibilities and opportunities. The development of transport, smart cities, urbanizations and leisure areas can be improved through the use of distributed intelligent computer systems. In this regard, edge platforms or fog computing help increase efficiency, reduce network latency, improve security and bring intelligence to the edge of the network, the sensors, users and the environment. Several use cases of intelligent systems will be presented, and it will be analysed how the processes of implementation and use have been optimized by means of different tools