44 research outputs found
Grid coevolution for adaptive simulations; application to the building of opening books in the game of Go
International audienceThis paper presents a successful application of parallel (grid) coevolution applied to the building of an opening book (OB) in 9x9 Go. Known sayings around the game of Go are refound by the algorithm, and the resulting program was also able to credibly comment openings in professional games of 9x9 Go. Interestingly, beyond the application to the game of Go, our algorithm can be seen as a âmetaâ-level for the UCT-algorithm: âUCT applied to UCTâ (instead of âUCT applied to a random playerâ as usual), in order to build an OB. It is generic and could be applied as well for analyzing a given situation of a Markov Decision Process
Evolutionary Optimisation for Obstacle Detection and Avoidance in Mobile Robotics
http://www.fujipress.jpThis paper presents an artificial evolution-based method for stereo image analysis and its application to real-time obstacle detection and avoidance for a mobile robot. It uses the Parisian approach, which consists here in splitting the representation of the robot's environment into a large number of simple primitives, the âfliesâ, which are evolved according to a biologically inspired scheme. Results obtained on real scene with different fitness functions are presented and discussed, and an exploitation for obstacle avoidance in mobile robotics is proposed
Grid coevolution for adaptive simulations; application to the building of opening books in the game of Go
International audienceThis paper presents a successful application of parallel (grid) coevolution applied to the building of an opening book (OB) in 9x9 Go. Known sayings around the game of Go are refound by the algorithm, and the resulting program was also able to credibly comment openings in professional games of 9x9 Go. Interestingly, beyond the application to the game of Go, our algorithm can be seen as a âmetaâ-level for the UCT-algorithm: âUCT applied to UCTâ (instead of âUCT applied to a random playerâ as usual), in order to build an OB. It is generic and could be applied as well for analyzing a given situation of a Markov Decision Process
Modeling an agrifood industrial process using cooperative coevolution Algorithms
This report presents two experiments related to the modeling of an industrial agrifood process using evolutionary techniques. Experiments have been focussed on a specific problem which is the modeling of a Camembert-cheese ripening process. Two elated complex optimisation problems have been considered: -- a deterministic modeling problem, the phase prediction roblem, for which a search for a closed form tree expression has been performed using genetic programming (GP), -- a Bayesian network structure estimation problem, considered as a two-stage problem, i.e. searching first for an approximation of an independence model using EA, and then deducing, via a deterministic algorithm, a Bayesian network which represents the equivalence class of the independence model found at the first stage. In both of these problems, cooperative-coevolution techniques (also called ``Parisian'' approaches) have been proved successful. These approaches actually allow to represent the searched solution as an aggregation of several individuals (or even as a whole population), as each individual only bears a part of the searched solution. This scheme allows to use the artificial Darwinism principles in a more economic way, and the gain in terms of robustness and efficiency is important
Artificial Darwinism: an overview
Genetic algorithms, genetic programming, evolution strategies, and what is now called evolutionary algorithms, are
stochastic optimisation techniques inspired by Darwinâs theory. We present here an overview of these techniques, while
stressing on the extreme versatility of the artificial evolution concept. Their applicative framework is very large and is not
limited to pure optimisation. Artifical evolution implementations are however computationally expensive: an efficient
tuning of the components and parameter of these algorithms should be based on a clear comprehension of the
evolutionary mechanisms. Moreover, it is noticeable that the killer-applications of the domain are for the most part based
on hybridisation with other optimisation techniques. As a consequence, evolutionary algorithms are not to be considered
in competition but rather in complement to the âclassical â optimisation techniques.Les algorithmes gĂ©nĂ©tiques, la programmation gĂ©nĂ©tique, les stratĂ©gies dâĂ©volution, et ce que lâon appelle
maintenant en gĂ©nĂ©ral les algorithmes Ă©volutionnaires, sont des techniques dâoptimisation stochastiques
inspirĂ©es de la thĂ©orie de lâĂ©volution selon Darwin. Nous donnons ici une vision globale de ces techniques,
en insistant sur lâextrĂȘme flexibilitĂ© du concept dâĂ©volution artificielle. Cet outil a un champ trĂšs vaste
dâapplications, qui ne se limite pas Ă lâoptimisation pure. Leur mise en oeuvre se fait cependant au prix dâun
coĂ»t calculatoire important, dâoĂč la nĂ©cessitĂ© de bien comprendre ces mĂ©canismes dâĂ©volution pour
adapter et régler efficacement les différentes composantes de ces algorithmes. Par ailleurs, on note que les
applications-phares de ce domaine sont assez souvent fondĂ©es sur une hybridation avec dâautres techniques
dâoptimisation. Les algorithmes Ă©volutionnaires ne sont donc pas Ă considĂ©rer comme une mĂ©thode
dâoptimisation concurrente des mĂ©thodes dâoptimisation classiques, mais plutĂŽt comme une approche
complémentaire
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
Regession Methods in Traffic Prediction
DiplomovĂĄ prĂĄce se zabĂœvĂĄ moĆŸnostmi predikce dopravnĂ situace na makroskopickĂ© Ășrovni s vyuĆŸitĂm ĂșdajĆŻ namÄĆenĂœch pomocĂ dopravnĂch senzorĆŻ. TÄmito senzory mohou bĂœt indukÄnĂ smyÄky, radarovĂ© detektory nebo kamery. PrĂĄce se zamÄĆuje na problematiku predikce dojezdovĂœch dob automobilĆŻ. V rĂĄmci diplomovĂ© prĂĄce byla navrĆŸena a implementovĂĄna metoda dojezdovĂœch dob. NavrĆŸenĂĄ metoda byla otestovĂĄna pomocĂ dat z reĂĄlnĂ©ho provozu. PrvnĂm cĂlem prĂĄce bude seznĂĄmenĂ s metodami predikce, kterĂ© budou vyuĆŸĂvĂĄny. HlavnĂm cĂlem prĂĄce je vyuĆŸĂt zĂskanĂœch znalostĂ k navrĆŸenĂ a implementaci aplikace, kterĂĄ bude predikovat poĆŸadovanĂ© dopravnĂ veliÄiny.Master thesis deals with possibilities of predicting traffic situation on the macroscopic level using data, that were recorded using traffic sensors. This sensors could be loop detectors, radar detectors or cameras. The main problem discussed in this thesis is the travel time of cars. A method for travel time prediction was designed and implemented as a part of this thesis. Data from real traffic were used to test the designed method. The first objective of this thesis is to become familiar with the prediction methods that will be used. The main objective is to use the acquired knowledge to design and to implement an aplication that will predict required traffic variables.