25,870 research outputs found
Pattern languages in HCI: A critical review
This article presents a critical review of patterns and pattern languages in human-computer interaction (HCI). In recent years, patterns and pattern languages have received considerable attention in HCI for their potential as a means for developing and communicating information and knowledge to support good design. This review examines the background to patterns and pattern languages in HCI, and seeks to locate pattern languages in relation to other approaches to interaction design. The review explores four key issues: What is a pattern? What is a pattern language? How are patterns and pattern languages used? and How are values reflected in the pattern-based approach to design? Following on from the review, a future research agenda is proposed for patterns and pattern languages in HCI
An overview of decision table literature.
The present report contains an overview of the literature on decision tables since its origin. The goal is to analyze the dissemination of decision tables in different areas of knowledge, countries and languages, especially showing these that present the most interest on decision table use. In the first part a description of the scope of the overview is given. Next, the classification results by topic are explained. An abstract and some keywords are included for each reference, normally provided by the authors. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. Other examined topics are the theoretical or practical feature of each document, as well as its origin country and language. Finally, the main body of the paper consists of the ordered list of publications with abstract, classification and comments.
On Evaluating Commercial Cloud Services: A Systematic Review
Background: Cloud Computing is increasingly booming in industry with many
competing providers and services. Accordingly, evaluation of commercial Cloud
services is necessary. However, the existing evaluation studies are relatively
chaotic. There exists tremendous confusion and gap between practices and theory
about Cloud services evaluation. Aim: To facilitate relieving the
aforementioned chaos, this work aims to synthesize the existing evaluation
implementations to outline the state-of-the-practice and also identify research
opportunities in Cloud services evaluation. Method: Based on a conceptual
evaluation model comprising six steps, the Systematic Literature Review (SLR)
method was employed to collect relevant evidence to investigate the Cloud
services evaluation step by step. Results: This SLR identified 82 relevant
evaluation studies. The overall data collected from these studies essentially
represent the current practical landscape of implementing Cloud services
evaluation, and in turn can be reused to facilitate future evaluation work.
Conclusions: Evaluation of commercial Cloud services has become a world-wide
research topic. Some of the findings of this SLR identify several research gaps
in the area of Cloud services evaluation (e.g., the Elasticity and Security
evaluation of commercial Cloud services could be a long-term challenge), while
some other findings suggest the trend of applying commercial Cloud services
(e.g., compared with PaaS, IaaS seems more suitable for customers and is
particularly important in industry). This SLR study itself also confirms some
previous experiences and reveals new Evidence-Based Software Engineering (EBSE)
lessons
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
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A bibliography on parallel and vector numerical algorithms
This is a bibliography of numerical methods. It also includes a number of other references on machine architecture, programming language, and other topics of interest to scientific computing. Certain conference proceedings and anthologies which have been published in book form are listed also
Strategic Issues, Problems and Challenges in Inductive Theorem Proving
Abstract(Automated) Inductive Theorem Proving (ITP) is a challenging field in automated reasoning and theorem proving. Typically, (Automated) Theorem Proving (TP) refers to methods, techniques and tools for automatically proving general (most often first-order) theorems. Nowadays, the field of TP has reached a certain degree of maturity and powerful TP systems are widely available and used. The situation with ITP is strikingly different, in the sense that proving inductive theorems in an essentially automatic way still is a very challenging task, even for the most advanced existing ITP systems. Both in general TP and in ITP, strategies for guiding the proof search process are of fundamental importance, in automated as well as in interactive or mixed settings. In the paper we will analyze and discuss the most important strategic and proof search issues in ITP, compare ITP with TP, and argue why ITP is in a sense much more challenging. More generally, we will systematically isolate, investigate and classify the main problems and challenges in ITP w.r.t. automation, on different levels and from different points of views. Finally, based on this analysis we will present some theses about the state of the art in the field, possible criteria for what could be considered as substantial progress, and promising lines of research for the future, towards (more) automated ITP
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