180 research outputs found
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 Masterâslave 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 â 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 â 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
DELICIOUS: Deadline-Aware Approximate Computing in Cache-Conscious Multicore
Enhancing result-accuracy in approximate computing (AC) based real-time systems, without violating power constraints of the underlying hardware, is a challenging problem. Execution of such AC real-time applications can be split into two parts: (i) the mandatory part , execution of which provides a result of acceptable quality, followed by (ii) the optional part , that can be executed partially or fully to refine the initially obtained result in order to increase the result-accuracy, without violating the time-constraint. This paper introduces DELICIOUS , a novel hybrid offline-online scheduling strategy for AC real-time dependent tasks. By employing an efficient heuristic algorithm , DELICIOUS first generates a schedule for a task-set with an objective to maximize the results-accuracy, while respecting system-wide constraints. During execution, DELICIOUS then introduces a prudential cache resizing that reduces temperature of the adjacent cores, by generating thermal buffers at the turned off cache ways. DELICIOUS further trades off this thermal benefits by enhancing the processing speed of the cores for a stipulated duration, called V/F Spiking , without violating the power budget of the core, to shorten the execution length of the tasks. This reduced runtime is exploited either to enhance result-accuracy by dynamically adjusting the optional part, or to reduce temperature by enabling sleep mode at the cores. While surpassing the prior art, DELICIOUS offers 80% result-accuracy with its scheduling strategy, which is further enhanced by 8.3% in online, while reducing runtime peak temperature by 5.8 âC on average, as shown by benchmark based evaluation on a 4-core based multicore
Journal of Telecommunications in Higher Education
In This Issue
6 Meeting Bandwidth Challenges on Campus
12 Voice-over-LAN: A Solution for Convergence in the Enterprise
20 Going Beyond Best Effort IP Networking
25 The Politics of Convergence
32 Pursuing the Promise of the Paperless Office
38 New Visions for University Cellular Service
44 Maintaining Excellence at UM
Journal of Telecommunications in Higher Education
In This Issue
6 Meeting Bandwidth Challenges on Campus
12 Voice-over-LAN: A Solution for Convergence in the Enterprise
20 Going Beyond Best Effort IP Networking
25 The Politics of Convergence
32 Pursuing the Promise of the Paperless Office
38 New Visions for University Cellular Service
44 Maintaining Excellence at UM
Methods for construction and analysis of computational models in systems biology: applications to the modelling of the heat shock response and the self-assembly of intermediate filaments
Systems biology is a new, emerging and rapidly developing, multidisciplinary
research field that aims to study biochemical and biological systems from
a holistic perspective, with the goal of providing a comprehensive, system-
level understanding of cellular behaviour. In this way, it addresses one of
the greatest challenges faced by contemporary biology, which is to compre-
hend the function of complex biological systems. Systems biology combines
various methods that originate from scientific disciplines such as molecu-
lar biology, chemistry, engineering sciences, mathematics, computer science
and systems theory. Systems biology, unlike âtraditionalâ biology, focuses
on high-level concepts such as: network, component, robustness, efficiency,
control, regulation, hierarchical design, synchronization, concurrency, and
many others. The very terminology of systems biology is âforeignâ to âtra-
ditionalâ biology, marks its drastic shift in the research paradigm and it
indicates close linkage of systems biology to computer science.
One of the basic tools utilized in systems biology is the mathematical
modelling of life processes tightly linked to experimental practice. The stud-
ies contained in this thesis revolve around a number of challenges commonly
encountered in the computational modelling in systems biology. The re-
search comprises of the development and application of a broad range of
methods originating in the fields of computer science and mathematics for
construction and analysis of computational models in systems biology. In
particular, the performed research is setup in the context of two biolog-
ical phenomena chosen as modelling case studies: 1) the eukaryotic heat
shock response and 2) the in vitro self-assembly of intermediate filaments,
one of the main constituents of the cytoskeleton. The range of presented
approaches spans from heuristic, through numerical and statistical to ana-
lytical methods applied in the effort to formally describe and analyse the
two biological processes. We notice however, that although applied to cer-
tain case studies, the presented methods are not limited to them and can
be utilized in the analysis of other biological mechanisms as well as com-
plex systems in general. The full range of developed and applied modelling
techniques as well as model analysis methodologies constitutes a rich mod-
elling framework. Moreover, the presentation of the developed methods,
their application to the two case studies and the discussions concerning
their potentials and limitations point to the difficulties and challenges one
encounters in computational modelling of biological systems. The problems
of model identifiability, model comparison, model refinement, model inte-
gration and extension, choice of the proper modelling framework and level
of abstraction, or the choice of the proper scope of the model run through
this thesis
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