18 research outputs found
Packets routing and bandwidth sensing in a network traffic: Ant colony optimization tactic.
Packets routing and bandwidth sensing in a network platform remains an integral part of the study of signal flow.The algorithm to route packets in a network link called the AntNet algorithm was inspired by the behavior of real ant colonies. At each node in the network, a forward ant deposits some amount of pheromones at different links that responds to the node’s queue length. In this paper, we propose the inclusion of the computation of paths to adapt with the Depth Search Ant Explorer Network (DS-ANTENet) algorithm for discrete problems as an IP based mechanism. This method is tested and the efficiency is compared to the original AntNet algorithm and the Link-State algorithm to check the transmission of computing traffic flows between the nodes. We then made comparison with the algorithms proposed in the literature. The protocols were sorted out in terms of average number of lost packets ranging from the higher priority queue to the lower priority queue which then resulted to the fact that; First, AntNetBW (loss ratios reduction of 9.6% when compared to the AntNet and the Link-State algorithm respectively. Secondly, SANTENetBW (loss ratios reduction of 8.3% and 36.7% when compared to the AntNet and the Link-State algorithm respectively. Finally, DS-ANTENet (loss ratios reduction 0.7% and 33.2% when compared to the AntNet and the Link- State algorithm respectively.Keywords: Packets Routing, Bandwidth Sensing, Network Traffic, Ant Colony Optimization Algorithm, AntNe
Pheromone deposition/updating strategy in a network: using ant colony optimization (ACO) approach
The study and understanding of the social behavior of insects has contributed to the definition of some algorithms that are capable of solving several types of optimization problems. The most important and challenging problems that the ants encounters when routing through a network arc, is their ability to searching for the path with a shorter length as well as to minimize the total cost incurred in the process of routing through the network. In this paper, we introduced some features to the existing Ant Colony Optimization (ACO) algorithm to help tackle this problem. First, we defined two kinds of pheromone and then we also defined three kinds of heuristic information to guide the searching direction of ants for this bi-criteria problem. Each of the ants uses the heuristic types and the pheromone types in each iteration based on the probability, controlled by two parameters. These two parameters are adaptively adjusted in the process of the algorithm. Second, we used the information of the partial solutions to modify the bias of ants so that inferior choices will be ignored. Finally, we tested the performance of the experimental results of the algorithm in an application under different Deadline constraints and the performance of the algorithm prove to be more promising, for it outperformed the performance of most of the algorithm we downloaded on line.Keywords: Ant Colony Optimization algorithm, Pheromone Deposition, Pheromone Updating strategy, Cost Minimization, Network Routing, Optimization problem.
A Classification of Hyper-heuristic Approaches
The current state of the art in hyper-heuristic research comprises a set of approaches that share the common goal of automating the design and adaptation of heuristic methods to solve hard computational search problems. The main goal is to produce more generally applicable search methodologies. In this chapter we present and overview of previous categorisations of hyper-heuristics and provide a unified classification and definition which captures the work that is being undertaken in this field. We distinguish between two main hyper-heuristic categories: heuristic selection and heuristic generation. Some representative examples of each category are discussed in detail. Our goal is to both clarify the main features of existing techniques and to suggest new directions for hyper-heuristic research
Automated Design of Metaheuristic Algorithms: A Survey
Metaheuristics have gained great success in academia and practice because
their search logic can be applied to any problem with available solution
representation, solution quality evaluation, and certain notions of locality.
Manually designing metaheuristic algorithms for solving a target problem is
criticized for being laborious, error-prone, and requiring intensive
specialized knowledge. This gives rise to increasing interest in automated
design of metaheuristic algorithms. With computing power to fully explore
potential design choices, the automated design could reach and even surpass
human-level design and could make high-performance algorithms accessible to a
much wider range of researchers and practitioners. This paper presents a broad
picture of automated design of metaheuristic algorithms, by conducting a survey
on the common grounds and representative techniques in terms of design space,
design strategies, performance evaluation strategies, and target problems in
this field
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
Hyper-heuristic decision tree induction
A hyper-heuristic is any algorithm that searches or operates in the space of
heuristics as opposed to the space of solutions. Hyper-heuristics are
increasingly used in function and combinatorial optimization. Rather than
attempt to solve a problem using a fixed heuristic, a hyper-heuristic
approach attempts to find a combination of heuristics that solve a problem
(and in turn may be directly suitable for a class of problem instances).
Hyper-heuristics have been little explored in data mining. This work presents
novel hyper-heuristic approaches to data mining, by searching a space of
attribute selection criteria for decision tree building algorithm. The search is
conducted by a genetic algorithm. The result of the hyper-heuristic search in
this case is a strategy for selecting attributes while building decision trees.
Most hyper-heuristics work by trying to adapt the heuristic to the state of
the problem being solved. Our hyper-heuristic is no different. It employs a
strategy for adapting the heuristic used to build decision tree nodes
according to some set of features of the training set it is working on. We
introduce, explore and evaluate five different ways in which this problem
state can be represented for a hyper-heuristic that operates within a decisiontree
building algorithm. In each case, the hyper-heuristic is guided by a rule
set that tries to map features of the data set to be split by the decision tree
building algorithm to a heuristic to be used for splitting the same data set.
We also explore and evaluate three different sets of low-level heuristics that
could be employed by such a hyper-heuristic.
This work also makes a distinction between specialist hyper-heuristics and
generalist hyper-heuristics. The main difference between these two hyperheuristcs
is the number of training sets used by the hyper-heuristic genetic
algorithm. Specialist hyper-heuristics are created using a single data set from
a particular domain for evolving the hyper-heurisic rule set. Such algorithms
are expected to outperform standard algorithms on the kind of data set used
by the hyper-heuristic genetic algorithm. Generalist hyper-heuristics are
trained on multiple data sets from different domains and are expected to
deliver a robust and competitive performance over these data sets when
compared to standard algorithms.
We evaluate both approaches for each kind of hyper-heuristic presented in
this thesis. We use both real data sets as well as synthetic data sets. Our
results suggest that none of the hyper-heuristics presented in this work are
suited for specialization – in most cases, the hyper-heuristic’s performance on
the data set it was specialized for was not significantly better than that of
the best performing standard algorithm. On the other hand, the generalist
hyper-heuristics delivered results that were very competitive to the best
standard methods. In some cases we even achieved a significantly better
overall performance than all of the standard methods