A Study in function optimization with the breeder genetic algorithm

Optimization is concerned with the finding of global optima (hence the name) of problems that can be cast in the form of a function of several variables and constraints thereof. Among the searching methods, {em Evolutionary Algorithms} have been shown to be adaptable and general tools that have often outperformed traditional {em ad hoc} methods. The {em Breeder Genetic Algorithm} (BGA) combines a direct representation with a nice conceptual simplicity. This work contains a general description of the algorithm and a detailed study on a collection of function optimization tasks. The results show that the BGA is a powerful and reliable searching algorithm. The main discussion concerns the choice of genetic operators and their parameters, among which the family of Extended Intermediate Recombination (EIR) is shown to stand out. In addition, a simple method to dynamically adjust the operator is outlined and found to greatly improve on the already excellent overall performance of the algorithm.Postprint (published version

Evolutionary algorithm-based analysis of gravitational microlensing lightcurves

A new algorithm developed to perform autonomous fitting of gravitational microlensing lightcurves is presented. The new algorithm is conceptually simple, versatile and robust, and parallelises trivially; it combines features of extant evolutionary algorithms with some novel ones, and fares well on the problem of fitting binary-lens microlensing lightcurves, as well as on a number of other difficult optimisation problems. Success rates in excess of 90% are achieved when fitting synthetic though noisy binary-lens lightcurves, allowing no more than 20 minutes per fit on a desktop computer; this success rate is shown to compare very favourably with that of both a conventional (iterated simplex) algorithm, and a more state-of-the-art, artificial neural network-based approach. As such, this work provides proof of concept for the use of an evolutionary algorithm as the basis for real-time, autonomous modelling of microlensing events. Further work is required to investigate how the algorithm will fare when faced with more complex and realistic microlensing modelling problems; it is, however, argued here that the use of parallel computing platforms, such as inexpensive graphics processing units, should allow fitting times to be constrained to under an hour, even when dealing with complicated microlensing models. In any event, it is hoped that this work might stimulate some interest in evolutionary algorithms, and that the algorithm described here might prove useful for solving microlensing and/or more general model-fitting problems.Comment: 14 pages, 3 figures; accepted for publication in MNRA

Process Planning for Assembly and Hybrid Manufacturing in Smart Environments

Manufacturers strive for efficiently managing the consequences arising from the product proliferation during the entire product life cycle. New manufacturing trends such as smart manufacturing (Industry 4.0) present a substantial opportunity for managing variety. The main objective of this research is to help the manufacturers with handling the challenges arising from the product variety by utilizing the technological advances of the new manufacturing trends. This research focuses mainly on the process planning phase. This research aims at developing novel process planning methods for utilizing the technological advances accompanied by the new manufacturing trends such as smart manufacturing (Industry 4.0) in order to manage the product variety. The research has successfully addressed the macro process planning of a product family for two manufacturing domains: assembly and hybrid manufacturing. A new approach was introduced for assembly sequencing based on the notion of soft-wired galled networks used in evolutionary studies in Biological and phylogenetic sciences. A knowledge discovery model was presented by exploiting the assembly sequence data records of the legacy products in order to extract the embedded knowledge in such data and use it to speed up the assembly sequence planning. The new approach has the capability to overcome the critical limitation of assembly sequence retrieval methods that are not able to capture more than one assembly sequence for a given product. A novel genetic algorithm-based model was developed for that purpose. The extracted assembly sequence network is representing alternative assembly sequences. These alternative assembly sequences can be used by a smart system in which its components are connected together through a wireless sensor network to allow a smart material handling system to change its routing in case any disruptions happened. A novel concept in the field of product variety management by generating product family platforms and process plans for customization into different product variants utilizing additive and subtractive processes is introduced for the first time. A new mathematical programming optimization model is proposed. The model objective is to provide the optimum selection of features that can form a single product platform and the processes needed to customize this platform into different product variants that fall within the same product family, taking into consideration combining additive and subtractive manufacturing. For multi-platform and their associated process plans, a phylogenetic median-joining network algorithm based model is used that can be utilized in case of the demand and the costs are unknown. Furthermore, a novel genetic algorithm-based model is developed for generating multi-platform, and their associated process plans in case of the demand and the costs are known. The model\u27s objective is to minimize the total manufacturing cost. The developed models were applied on examples of real products for demonstration and validation. Moreover, comparisons with related existing methods were conducted to demonstrate the superiority of the developed models. The outcomes of this research provide efficient and easy to implement process planning for managing product variety benefiting from the advances in the technology of the new manufacturing trends. The developed models and methods present a package of variety management solutions that can significantly support manufacturers at the process planning stage

Islands of Fitness Compact Genetic Algorithm for Rapid In-Flight Control Learning in a Flapping-Wing Micro Air Vehicle: A Search Space Reduction Approach

On-going effective control of insect-scale Flapping-Wing Micro Air Vehicles could be significantly advantaged by active in-flight control adaptation. Previous work demonstrated that in simulated vehicles with wing membrane damage, in-flight recovery of effective vehicle attitude and vehicle position control precision via use of an in-flight adaptive learning oscillator was possible. Most recent approaches to this problem employ an island-of-fitness compact genetic algorithm (ICGA) for oscillator learning. The work presented provides the details of a domain specific search space reduction approach implemented with existing ICGA and its effect on the in-flight learning time. Further, it will be demonstrated that the proposed search space reduction methodology is effective in producing an error correcting oscillator configuration rapidly, online, while the vehicle is in normal service

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

Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review

Human gait is a complex activity that requires high coordination between the central nervous system, the limb, and the musculoskeletal system. More research is needed to understand the latter coordination\u27s complexity in designing better and more effective rehabilitation strategies for gait disorders. Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) are among the most used technologies for monitoring brain activities due to portability, non-invasiveness, and relatively low cost compared to others. Fusing EEG and fNIRS is a well-known and established methodology proven to enhance brain–computer interface (BCI) performance in terms of classification accuracy, number of control commands, and response time. Although there has been significant research exploring hybrid BCI (hBCI) involving both EEG and fNIRS for different types of tasks and human activities, human gait remains still underinvestigated. In this article, we aim to shed light on the recent development in the analysis of human gait using a hybrid EEG-fNIRS-based BCI system. The current review has followed guidelines of preferred reporting items for systematic reviews and meta-Analyses (PRISMA) during the data collection and selection phase. In this review, we put a particular focus on the commonly used signal processing and machine learning algorithms, as well as survey the potential applications of gait analysis. We distill some of the critical findings of this survey as follows. First, hardware specifications and experimental paradigms should be carefully considered because of their direct impact on the quality of gait assessment. Second, since both modalities, EEG and fNIRS, are sensitive to motion artifacts, instrumental, and physiological noises, there is a quest for more robust and sophisticated signal processing algorithms. Third, hybrid temporal and spatial features, obtained by virtue of fusing EEG and fNIRS and associated with cortical activation, can help better identify the correlation between brain activation and gait. In conclusion, hBCI (EEG + fNIRS) system is not yet much explored for the lower limb due to its complexity compared to the higher limb. Existing BCI systems for gait monitoring tend to only focus on one modality. We foresee a vast potential in adopting hBCI in gait analysis. Imminent technical breakthroughs are expected using hybrid EEG-fNIRS-based BCI for gait to control assistive devices and Monitor neuro-plasticity in neuro-rehabilitation. However, although those hybrid systems perform well in a controlled experimental environment when it comes to adopting them as a certified medical device in real-life clinical applications, there is still a long way to go

A Novel Hybrid Dimensionality Reduction Method using Support Vector Machines and Independent Component Analysis

Due to the increasing demand for high dimensional data analysis from various applications such as electrocardiogram signal analysis and gene expression analysis for cancer detection, dimensionality reduction becomes a viable process to extracts essential information from data such that the high-dimensional data can be represented in a more condensed form with much lower dimensionality to both improve classification accuracy and reduce computational complexity. Conventional dimensionality reduction methods can be categorized into stand-alone and hybrid approaches. The stand-alone method utilizes a single criterion from either supervised or unsupervised perspective. On the other hand, the hybrid method integrates both criteria. Compared with a variety of stand-alone dimensionality reduction methods, the hybrid approach is promising as it takes advantage of both the supervised criterion for better classification accuracy and the unsupervised criterion for better data representation, simultaneously. However, several issues always exist that challenge the efficiency of the hybrid approach, including (1) the difficulty in finding a subspace that seamlessly integrates both criteria in a single hybrid framework, (2) the robustness of the performance regarding noisy data, and (3) nonlinear data representation capability. This dissertation presents a new hybrid dimensionality reduction method to seek projection through optimization of both structural risk (supervised criterion) from Support Vector Machine (SVM) and data independence (unsupervised criterion) from Independent Component Analysis (ICA). The projection from SVM directly contributes to classification performance improvement in a supervised perspective whereas maximum independence among features by ICA construct projection indirectly achieving classification accuracy improvement due to better intrinsic data representation in an unsupervised perspective. For linear dimensionality reduction model, I introduce orthogonality to interrelate both projections from SVM and ICA while redundancy removal process eliminates a part of the projection vectors from SVM, leading to more effective dimensionality reduction. The orthogonality-based linear hybrid dimensionality reduction method is extended to uncorrelatedness-based algorithm with nonlinear data representation capability. In the proposed approach, SVM and ICA are integrated into a single framework by the uncorrelated subspace based on kernel implementation. Experimental results show that the proposed approaches give higher classification performance with better robustness in relatively lower dimensions than conventional methods for high-dimensional datasets

An Adaptive Modular Redundancy Technique to Self-regulate Availability, Area, and Energy Consumption in Mission-critical Applications

As reconfigurable devices\u27 capacities and the complexity of applications that use them increase, the need for self-reliance of deployed systems becomes increasingly prominent. A Sustainable Modular Adaptive Redundancy Technique (SMART) composed of a dual-layered organic system is proposed, analyzed, implemented, and experimentally evaluated. SMART relies upon a variety of self-regulating properties to control availability, energy consumption, and area used, in dynamically-changing environments that require high degree of adaptation. The hardware layer is implemented on a Xilinx Virtex-4 Field Programmable Gate Array (FPGA) to provide self-repair using a novel approach called a Reconfigurable Adaptive Redundancy System (RARS). The software layer supervises the organic activities within the FPGA and extends the self-healing capabilities through application-independent, intrinsic, evolutionary repair techniques to leverage the benefits of dynamic Partial Reconfiguration (PR). A SMART prototype is evaluated using a Sobel edge detection application. This prototype is shown to provide sustainability for stressful occurrences of transient and permanent fault injection procedures while still reducing energy consumption and area requirements. An Organic Genetic Algorithm (OGA) technique is shown capable of consistently repairing hard faults while maintaining correct edge detector outputs, by exploiting spatial redundancy in the reconfigurable hardware. A Monte Carlo driven Continuous Markov Time Chains (CTMC) simulation is conducted to compare SMART\u27s availability to industry-standard Triple Modular Technique (TMR) techniques. Based on nine use cases, parameterized with realistic fault and repair rates acquired from publically available sources, the results indicate that availability is significantly enhanced by the adoption of fast repair techniques targeting aging-related hard-faults. Under harsh environments, SMART is shown to improve system availability from 36.02% with lengthy repair techniques to 98.84% with fast ones. This value increases to five nines (99.9998%) under relatively more favorable conditions. Lastly, SMART is compared to twenty eight standard TMR benchmarks that are generated by the widely-accepted BL-TMR tools. Results show that in seven out of nine use cases, SMART is the recommended technique, with power savings ranging from 22% to 29%, and area savings ranging from 17% to 24%, while still maintaining the same level of availability