19,499 research outputs found
A Model-Derivation Framework for Software Analysis
Model-based verification allows to express behavioral correctness conditions
like the validity of execution states, boundaries of variables or timing at a
high level of abstraction and affirm that they are satisfied by a software
system. However, this requires expressive models which are difficult and
cumbersome to create and maintain by hand. This paper presents a framework that
automatically derives behavioral models from real-sized Java programs. Our
framework builds on the EMF/ECore technology and provides a tool that creates
an initial model from Java bytecode, as well as a series of transformations
that simplify the model and eventually output a timed-automata model that can
be processed by a model checker such as UPPAAL. The framework has the following
properties: (1) consistency of models with software, (2) extensibility of the
model derivation process, (3) scalability and (4) expressiveness of models. We
report several case studies to validate how our framework satisfies these
properties.Comment: In Proceedings MARS 2017, arXiv:1703.0581
A Model-Derivation Framework for Software Analysis
Model-based verification allows to express behavioral correctness conditions
like the validity of execution states, boundaries of variables or timing at a
high level of abstraction and affirm that they are satisfied by a software
system. However, this requires expressive models which are difficult and
cumbersome to create and maintain by hand. This paper presents a framework that
automatically derives behavioral models from real-sized Java programs. Our
framework builds on the EMF/ECore technology and provides a tool that creates
an initial model from Java bytecode, as well as a series of transformations
that simplify the model and eventually output a timed-automata model that can
be processed by a model checker such as UPPAAL. The framework has the following
properties: (1) consistency of models with software, (2) extensibility of the
model derivation process, (3) scalability and (4) expressiveness of models. We
report several case studies to validate how our framework satisfies these
properties.Comment: In Proceedings MARS 2017, arXiv:1703.0581
CU2CL: A CUDA-to-OpenCL Translator for Multi- and Many-core Architectures
The use of graphics processing units (GPUs) in
high-performance parallel computing continues to become more
prevalent, often as part of a heterogeneous system. For years,
CUDA has been the de facto programming environment for
nearly all general-purpose GPU (GPGPU) applications. In spite
of this, the framework is available only on NVIDIA GPUs,
traditionally requiring reimplementation in other frameworks
in order to utilize additional multi- or many-core devices.
On the other hand, OpenCL provides an open and vendorneutral
programming environment and runtime system. With
implementations available for CPUs, GPUs, and other types of
accelerators, OpenCL therefore holds the promise of a “write
once, run anywhere” ecosystem for heterogeneous computing.
Given the many similarities between CUDA and OpenCL,
manually porting a CUDA application to OpenCL is typically
straightforward, albeit tedious and error-prone. In response
to this issue, we created CU2CL, an automated CUDA-to-
OpenCL source-to-source translator that possesses a novel design
and clever reuse of the Clang compiler framework. Currently,
the CU2CL translator covers the primary constructs found in
CUDA runtime API, and we have successfully translated many
applications from the CUDA SDK and Rodinia benchmark suite.
The performance of our automatically translated applications via
CU2CL is on par with their manually ported countparts
A Field Guide to Genetic Programming
xiv, 233 p. : il. ; 23 cm.Libro ElectrĂłnicoA 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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