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A Parallel and Distributed Genetic-Based Learning
Classifier System with Application in Human
Electroencephalographic Signal Classification
Bradley Skinner
Doctor of Philosophy (Engineering)
Year of Submission: 2007
Centre for Health Technologies
Mechatronics and Intelligent Systems Group
Faculty of Engineering
University of Technology, Sydney
Australia
_«T* UNIVERSITY OFTECHNOLOGY SYDNEY V____________________
Contents
Introduction.............................................................................................................................23
1.1 Problem Statement.....................................................................................................23
1.2 Aim...........................................................................................................................24
1.3 Overview of the Study.............................................................................................. 25
1.4 Research Methods.....................................................................................................28
Review of Literature............................................................................................................... 30
2.1 Introduction............................................................................................................. 30
2.2 Genetic Algorithms................................................................................................. 31
2.2.1 Why use Genetic Algorithms?............................................................................ 32
2.2.2 Basic Definitions and Terminology.................................................................... 33
2.2.3 Selection Operators............................................................................................. 36
2.2.4 Recombination Operators.................................................................................... 38
2.2.5 Mutation Operators............................................................................................. 39
2.2.6 Static and Adaptive Operators............................................................................. 40
2.2.7 Static Crossover and Mutation Operators........................................................... 40
2.2.8 Dynamic Crossover and Mutation Operators...................................................... 41
2.2.9 Replacement Mechanisms................................................................................... 43
2.2.10 Coding Schemes.............................................................................................. 44
2.2.11 The Schema Theorem..................................................................................... 44
2.2.12 Building Block Hypothesis............................................................................. 47
2.2.13 The No Free Lunch Theorem........................................................................... 49
2.3 Parallel Genetic Algorithms.................................................................................... 50
2.3.1 Classification of Parallel Genetic Algorithms......................................................50
2.4 Parallel Computing Architectures............................................................................58
2.5 Inter-process Communication Models..................................................................... 60
2.5.1 Socket Programming............................................................................................60
2.5.2 Parallel Virtual Machine - PVM......................................................................... 61
2.5.3 Message Passing Interface - MPI.........................................................................61
2.5.4 Java Remote Method Invocation - JRMI............................................................ 62
2.5.5 Common Object Request Broker Architecture - CORBA................................... 62
2.5.6 Globus................................................................................................................. 62
2.6 Learning Classifier Systems.....................................................................................63
2.6.1 Reinforcement Learning...................................................................................... 64
2.6.2 Reinforcement Learning Architectures............................................................... 65
2.6.3 Tabular Q-Learning............................................................................................. 67
2.6.4 A Brief History of Learning Classifier Systems................................................. 68
2.6.5 Holland’s Learning Classifier System................................................................. 69
2.6.6 Zeroth Level Classifier System - ZCS................................................................ 72
2.6.7 Overview of the XCS Classifier System............................................................. 76
2.6.8 Detailed Description of the XCS Classifier System............................................ 78
2.6.9 XCS and Tabular Q-Learning............................................................................. 93
2.6.10 Common Classifier Encodings....................................................................... 94
2.7 Parallel Learning Classifier Systems....................................................................... 97
2.8 Conclusion............................................................................................................... 98
Classification Learning of the Human Electroencephalogram........................................ 101
3.1 Introduction............................................................................................................ 101
3.2 The Human Electroencephalogram........................................................................102
3.2.1 The Source of EEG Signals............................................................................... 102
3.2.2 Rhythms of the Human Brain.............................................................................108
3.2.3 Recording Electrodes.........................................................................................109
3.2.4 The EEG System................................................................................................115
3.2.5 EEG Artefacts....................................................................................................117
3.3 Correlation of Human EEG with Mental Tasks.....................................................120
3.4 Signal Processing and Representation of Human EEG..........................................123
3.4.1 Frequency-Based Representation.......................................................................123
3.4.2 Parametric Methods...........................................................................................124
3.4.3 Other Signal Processing and Representations....................................................125
3.5 Data Mining Human EEG......................................................................................126
3.5.1 Classification of EGG using Noil-Evolutionary Algorithms............................. 127
3.5.2 Learning Classifier Systems for Data Mining in Medical Databases............... 130
3.6 Conclusion.............................................................................................................. 133
Optimal Migration Strategies for a PGA with Adaptive Mutation................................. 136
CONTENTS 3
CONTENTS 4
Chapter Outline................................................................................................................... 136
4.1 Aim......................................................................................................................... 137
4.2 PGA Convergence Velocity and Solution Quality................................................. 137
4.2.1 Refinement.........................................................................................................138
4.2.2 Operational Hypotheses.....................................................................................139
4.3 Method.................................................................................................................... 139
4.3.1 Multi-Deme Parallel Genetic Algorithm............................................................ 140
4.3.2 Numerical Objective Functions..........................................................................148
4.3.3 Experiment Parameters......................................................................................151
4.3.4 Experimental Apparatus.....................................................................................152
4.4 Results.................................................................................................................... 153
4.4.1 Description.........................................................................................................153
4.4.2 Presentation........................................................................................................ 154
4.4.3 Analysis.............................................................................................................. 161
4.4.4 Interpretation......................................................................................................163
4.5 Conclusion.............................................................................................................. 169
Sequential Quadratic Programming Methods in PGAs....................................................171
Chapter Outline................................................................................................................... 171
5.1 Aim......................................................................................................................... 172
5.2 Performance Hypothesis of the Hybrid Algorithm................................................. 173
5.2.1 Refinement......................................................................................................... 173
5.3 Method.................................................................................................................... 175
5.3.1 The Hybrid Search Algorithm............................................................................175
5.3.2 Exploration Stage - Parallel Genetic Algorithm................................................ 177
5.3.3 Exploitation Stage - The RFSQP Algorithm..................................................... 182
5.3.4 Numerical Objective Functions..........................................................................186
5.3.5 Experiment Parameters......................................................................................189
5.3.6 Experimental Apparatus.....................................................................................191
5.4 Results.................................................................................................................... 191
5.4.1 Description......................................................................................................... 191
5.4.2 Presentation........................................................................................................ 192
5.4.3 Analysis.............................................................................................................. 196
5.4.4 Interpretation...................................................................................................... 198
5.5 Conclusion..............................................................................................................200
CONTENTS 5
Genetic-Based Machine Learning Classifier for EEG Signal Classification.................. 202
Chapter Outline...................................................................................................................202
6.1 Aim.........................................................................................................................203
6.2 EEG Signal Representation and Competitive Hypotheses.................................... 203
6.2.1 Refinement..........................................................................................................204
6.2.2 Operational Hypotheses.................................................................................... 206
6.3 Method....................................................................................................................206
6.3.1 Experimental Apparatus - EEG Data Acquisition............................................. 207
6.3.2 Participant Preparation.......................................................................................213
6.3.3 Mental Task Experiments................................................................................. 214
6.3.4 Data Acquisition and Pre-processing................................................................ 215
6.3.5 EEG Signal Representations............................................................................. 222
6.3.6 Classification Environment............................................................................... 231
6.4 Results....................................................................................................................236
6.4.1 Description........................................................................................................ 236
6.4.2 Presentation........................................................................................................237
6.4.3 Analysis..............................................................................................................242
6.4.4 Interpretation......................................................................................................245
6.5 Conclusion..............................................................................................................249
Distributed Classifier Migration in XCS for Classification of EEG Signals........... . 251
Chapter Outline...................................................................................................................251
7.1 Aim.........................................................................................................................252
7.2 Parallel Evolutionary Methods in Leaning Classifier Systems............................. 252
7.2.1 Refinement.................................................................. 253
7.2.2 Operational Hypotheses.....................................................................................254
7.3 Method....................................................................................................................255
7.3.1 Classification Environment - pXCS................................................................... 255
7.3.2 Parametric Representation of EEG Data............................................................ 261
7.3.3 Feature Vector Encoding................................................................................... 262
7.3.4 Experimental Apparatus.....................................................................................262
7.3.5 Experimental Parameters.................................................................................. 263
7.4 Results....................................................................................................................264
7.4.1 Description.........................................................................................................264
7.4.2 Presentation........................................................................................................266
7.4.3 Analysis..............................................................................................................273
CONTENTS 6
7.4.4 Interpretation..................................................................................................... 282
7.5 Conclusion.................................................................................................................. 285
Summary and Conclusions................................................................................................... 287
8.1 Summary of Results.............................................................................................. 287
8.1.1 Findings on XCS performance for the Classification of EEG.......................... 287
8.1.2 Findings on Distributed Classifier Migration in pXCS for the Classification of
EEG ............................................................................................................................289
8.1.3 Findings on Optimal Migration Strategies in a PGA with Adaptive Mutation. 291
8.1.4 Findings on the Incorporation of the RFSQP method in PGAs........................ 292
8.2 Conclusions........................................................................................................... 294
8.2.1 Aim One............................................................................................................ 294
8.2.2 Aim Two........................................................................................................... 295
8.2.3 Aim Three......................................................................................................... 295
8.2.4 Aim Four........................................................................................................... 296
8.2.5 Aim Five........................................................................................................... 296
8.3 Direction of Future Work...................................................................................... 297
Bibliography...........................................................................................................................299
A Schema Theorem.............................................................................................................. 309
B Test Functions....................................................................................................................313
B.I Introduction........................................................................................................... 313
Sphere Function (fSph)..................................................................................................... 313
Rastrigin Function (fRas)................................................................................................. 314
Ackley Path Function (fAck)............................................................................................ 315
Schwefel Function (fSch)................................................................................................. 316
Michalewicz Function (fMic)........................................................................................... 317
Langerman Function (f^n)..............................................................................................318
Deceptive n-bit Trap Function (fTrap).............................................................................. 319
Royal Roads Function (fRR).............................................................................................320
C Taylors Series - RFSQP Algorithm................................................................................ 321
D The XCS Classifier........................................................................................................... 323
CONTENTS 7
E Genetic-Based Machine Learning Classifier for EEG Classification........................... 329
F The pXCS Classifier......................................................................................................... 337
G The Electroencephalographic System............................................................................ 340
G. 1 BioSemi Active Two Mk2 System Specification................................................... 340
G.2 BioSemi EEG Electrode Montage for 32+2 Channels........................................... 341
G.3 BioSemi BDF and EDF File Format...................................................................... 342
H Thesis Software Collection.............................................................................................. 343
List of Figures1: Taxonomy of Search Methods in the broader field of Computational Intelligence....................... 31
2: (a) One-Point Crossover Operation using gene position 3. (b) The Two-Point Crossover Operation
using gene positions 2 and 6................................................................................................................38
3: Uniform Crossover Operator..........................................................................................................39
4: The Mutation Operator selecting genes in offspring chromosomes............................................... 40
5: 4-bit Deceptive Trap Function........................................................................................................48
6: Schematic diagram of the Multiple-Population Coarse-Grained PGA consisting of six isolated
subpopulations......................................................................................................................................53
7: Schematic diagram of the Global Single-Population Master-Slave PGA.......................................56
8: Schematic diagram of the Single-Population Fine-Grained PGA arranged into neighbourhoods ..57
9: (a) Multi-deme coarse-grained PGA at both levels with different connection topologies, (b) Multi-
deme coarse-grained PGA at upper level and fine-grained PGA at lower level.................................57
10: The basic Reinforcement Learning Problem.................................................................................65
11: Reinforcement Learning Architectures - Policy-Only and Reinforcement-Comparison..............66
12: Reinforcement Learning Architectures - Adaptive-Heuristic-Critic and Q-Learning................ 66
13: Schematic illustration of Holland’s 1986 Learning Classifier System..........................................70
14: Schematic illustration of ZCS Learning Classifier System........................................................... 72
15: Schematic illustration of XCS Learning Classifier System, 1998.................................................79
16: Classifier accuracy k as a function of classifier prediction error e............................................... 89
1 7: The Cerebrum, showing the 4 lobes (Frontal, Parietal, Temporal, and Occipital)..................... 103
18: Structure of a typical neuron........................................................................................................ 104
19: The Purkinje cell and the Pyramidal cell located in the cerebral cortex..................................... 104
20: Postsynaptic and Action Potential of neuronal membrane..........................................................105
21: The generation of EEG by the cerebral cortex............................................................................107
22: Different types of normal EEG waves in the time domain (a) Delta, (b) Theta, (c) Alpha (d) Beta
activity (e) blocking of the alpha wave by eye opening and (f) 1 second markers........................... 109
23: Equivalent Circuit Model for an electrode.................................................................................. 1 12
24: Basic circuit model for two scalp electrodes with input into a differential amplifier.................112
25: Electrode names and placement in the International 10-20 System........................................... 1 14
26: Modified expanded 10-20 system................................................................................................115
27: Simplified Block Diagram of a modern 8-Channel EEG Recording System............................. 115
LIST OF FIGURES 9
28: A modern Sigma-Delta ADC with Fully programmable Low-Pass filter.................................. 117
29: Eye movement artefacts in time domain. (1) Blink, (2) eye opening (EO), (3) eye closing (EC),
rhythmical slow eye movement (EM), (5) saccadic eye movement (EM)........................................118
30: Common artefacts generated by extracerebral activity................................................................ 118
31: Non-biological artefacts............................................................................................................... 119
32: Mutation Probability PM(0...........................................................................................................14233: The Multi-Deme Master-Slave Topologies of the PGA using 4- and 8- subpopulations............142
34: Unordered and blocking communication provided synchronous migration between loosely-
coupled and coarse-grained sub-populations of the Master-Slave PGA...........................................144
35: Decomposition of the Level 1 (PGA) Structure Chart for the PGA Software System............... 146
36: Decomposition of Level 1.1 (Initialise), 1.3 (OptionalGAFuncs), and 1.4 (Cycle_Error) Structure
Charts for the PGA System................................................................................................................ 146
37: Decomposition of Level 1.2 (Generation) Structure Chart for the PGA Software System.........147
38: Decomposition of Level 1.5 (Migration) Structure Chart for the PGA Software System.......... 147
39: Decomposition of Level 1.6 (Write Re suits) Structure Chart for the PGA Software System...... 147
40: An example illustration of the Sphere Function for n=3..............................................................148
41: An example illustration of Rastrigin's Function for n=3 (top) and n=2 (bottom)........................149
42: An example illustration of the Ackley Path Function for n=3 (top) and n=2 (bottom)............... 150
43: Mutation Probability PM(t) for a = 5, 10, 20, 40, 80, 160............................................................152
44: Sphere Function with 4-subpopulations and variable migration period...................................... 155
45: Sphere Function with 8-subpopulations and variable migration period...................................... 155
46: Rastrigin’s Function with 4-subpopulations and variable migration period................................156
47: Rastrigin’s Function with 8-subpopulations and variable migration period................................156
48: Ackley’s Path Function with 4-subpopulations and variable migration period...........................157
49: Ackley’s Path Function with 8-subpopulations and variable migration period........................... 157
50: Adaptive Mutation vs Static Mutation probability for the Sphere Function (mF= 5, cl# = 8).......158
51: Adaptive Mutation vs Static Mutation probability for the Rastrigin Function (mF= 5, cl# = 8).... 159
52: Adaptive vs Static Mutation probability for the Ackley Path Function (mF=5, cl# = 8)..............160
53: The hybrid search algorithm architecture.....................................................................................17654: The Multi-Deme Master-Slave Topology of the PGA used in the exploration stage of the hybrid
algorithm.............................................................................................................................................17855: Unordered and blocking communication provided synchronous migration between loosely-
coupled and coarse-grained sub-populations of a 4-deme Master-Slave PA....................................179
56: Hybrid Algorithm Search - Exploration and Exploitation...........................................................184
57: Decomposition of the Level 1 Structure Chart for the Hybrid Algorithm Software System.......185
58: Decomposition of the Level 1.7 Structure Chart for the RFSQP algorithm................................ 185
LIST OF FIGURES 10
59: An example illustration of Schwefel’s Function for n=3 (top) and n=2 (bottom)....................... 187
60: An example illustration of the Michalewicz Function for n=3 (top) and n=2 (bottom).............. 188
61: An example illustration of the Langerman Function for n=3 (top) and n=2 (bottom)................. 189
62: Results for PGA and Hybrid Search Algorithm applied to the Sphere Function......................... 192
63: Results for PGA and Hybrid Search Algorithm applied to Rastrigin’s Function........................ 193
64: Results for PGA and Hybrid Search Algorithm applied to the Ackley Path Function............... 193
65: Results for PGA and Hybrid Search Algorithm applied to the Schwefel Function.................... 194
66: Results for PGA and Hybrid Search Algorithm applied to the Michalewicz Function.............. 194
67: Results for PGA and Hybrid Search Algorithm applied to the Langerman Function.................195
68: Experimental configuration of the BioSemi Active-Two EEG Acquisition subsystem..............207
69: Rear view of the BioSemi headcap used for all experiements in this study................................ 208
70: Circuit diagram and transfer function of the active electrode......................................................208
71: (a) BioSemi “pin-type” active electrode and (b) Standard set of 32 electrodes.......................... 209
72: BiosSemi ActiveTwo AD-Box and battery box power supply....................................................210
73: Front and Rear view of the ActiveTwo Optical-to-USB2 converter........................................... 211
74: The BioSemi ActiView Real-Time Interface.............................................................................. 212
75: Participant Preparation.................................................................................................................213
76: A fully prepared participant ready to commence mental task experiments................................. 214
77: An example of 32-channel referenced EEG data from t=9 to t=21 seconds for baseline and
Mental Counting task from a single participant................................................................................ 217
78: An example of 6-channel EEG data from t-9 to r=21 seconds for baseline and Mental Counting
Task from a single participant.......................................................................................................... 218
79: A 10-second recording of the Mental Counting Task being windowed to 8-seconds by removal of
the first and last 1-second of time-domain EEG data from all 6-channels........................................ 219
80: Partitioning EEG windows into segments of 4.0- and 2.0-seconds in length.............................. 220
81: Partitioning EEG windows into segments of 1.0- and 0.5-seconds in length.............................. 221
82: Generalised Autoregressive Filter Model of order p................................................................... 223
83: Example of visual AR order determination................................................................................. 224
84: AR order determination using the Akaike Information Criterion (AIC) Technique................... 225
85: Tree solution for the 9 asymmetry ratios of the Delta (8) EEG frequency band......................... 226
86: A single 108-bit feature vector based on the parametric representation......................................227
87: A single feature vector based on the frequency-band representation.......................................... 228
88: Summary of the steps required produce training and testing sets................................................229
89: A single pre-processed and windowed time-domain segment of EEG data is transformed into the
frequency-domain using FFT........................................................................................................... 230
90: Default settings for classifiers implemented in Weka v3.5.5......................................................233
LIST OF FIGURES 11
91: Weka v3.5.5 GUI; (a) The training data and (b) Classification result using supplied test set....234
92: Topologies employed by pXCS - (a) Uni-directional Ring, (b) Bi-directional ring and (c) Fully-
Connected topologies........................................................................................................................256
93: pXCS Migration System performs classifier selection and replacement upon the sub-population
[P], producing classifier sets [I] and [E]............................................................................................260
94: Average of the best classification accuracy with varying migration frequencies between pXCS
and XCS.............................................................................................................................................267
95: Average of the best classification accuracy with varying migration rates between pXCS and
XCS....................................................................................................................................................267
96: Average of the best classification accuracy between a fully connected pXCS topology and XCS
for each participant and associated task experiment..........................................................................268
97: Average of the best classification accuracy between a bi-directional ring pXCS topology and
XCS for each participant and associated task experiment................................................................ 268
98: Average of the best classification accuracy between a uni-directional ring pXCS topology and
XCS for each participant and associated task experiment................................................................ 269
99: Histogram of improvements and degradation in average classification accuracy between pXCS
and XCS for varying migration frequencies......................................................................................270
100: Histogram of improvements and degradation in average classification accuracy between pXCS
and XCS for varying migration rates................................................................................................. 270
101: Reduction in Classifier Population between pXCS and XCS during training with varying
migration frequencies.........................................................................................................................271
102: Reduction in Classifier Population between pXCS and XCS during training with varying
migration rates...................................... 271
103: Improvement in Learning Rate during training between pXCS and XCS with varying migration
frequencies......................................................................................................................................... 272
104: Improvement in Learning Rate during training between pXCS and XCS with varying migration
rates....................................................................................................................................................272
105:C!assification Accuracy of pXCS and XCS for a single experimental instance........................273
106: Two-sample pooled t-test results comparing the classification accuracy of pXCS vs XCS..... 274
107: Two-sample pooled t-test curve comparing the classification accuracy of pXCS vs XCS..... 276
108: Two-sample pooled t-test curve comparing the classification accuracy of pXCS vs XCS..... 276
109: Example of reduction in classifier population of pXCS and XCS............................................ 279
1 10: Example of learning rate as a fraction of correct classifications of pXCS and XCS................ 281
111: Two-sample pooled t-test curve comparing the classification accuracy of pXCS and XCS for
the results presented in Figure 110.................................................................................................... 281
I I2.B: Sphere Function (n=3), 113.B: Sphere Function Contour Plot (n=2)....................................313
LIST OF FIGURES 12
114. B: Rastrigin Function (n=3)....................................................................................................... 314
115. B: Rastrigin Function Contour Plot (n=2)...............................................................................314
116. B: Ackley Path Function (n=3)..................................................................................................315
117. B: Ackley Path Function Contour Plot (n=2)............................................................................315
118. B: Schwefel Function (n=3)...................................................................................................... 316
119. B: Schwefel Function Contour Plot (n=2).................................................................................316
120. B: Michalewicz Function (n=3)...............................................................................................317
121. B: Michalewicz Function Contour Plot (n=2).........................................................................317
122. B: Langerman Function (n=3)................................................................................................ 318
123. B: Langerman Function Contour Plot (n=2)........................................................................... 318
List of Tables1: Mapping between terms in Natural Evolution and the Genetic Algorithms Framework....34
2: Population of Micro-classifiers in XCS............................................................................... 81
3: Population of Macro-classifiers in XCS.............................................................................. 81
4: Datasets used for the Comparison of Learning Schemes in...............................................132
5: Average Prediction Accuracy and Standard Deviations of six Machine Learning Schemes
using Stratified Ten-Fold Cross-Validation............................................................................132
6: Parallel Genetic Algorithm Parameter Specification........................................................ 151
7: Cluster Computing Hardware and Software Environment - PGA.................................... 152
8: Convergence Velocity and Solution Quality - Sphere Function....................................... 155
9: Convergence Velocity and Solution Quality - Rastrigin Function................................... 156
10: Convergence Velocity and Solution Quality - Ackley Path Function............................157
11: Effect of Adaptive Mutation Operator (a) on Convergence Velocity and Solution Quality
- Sphere Function....................................................................................................................158
12: Effect of Adaptive Mutation Operator (CC) on Convergence Velocity and Solution Quality
- Rastrigin Function.................................................................................................................159
13: Effect of Adaptive Mutation Operator (a) on Convergence Velocity and Solution Quality
- Ackley Path Function...........................................................................................................160
14: Results For Convergence Velocity against Migration Frequency.................................... 164
15: Results For Solution Quality Against Migration Frequency............................................ 164
16: Results for Convergence Velocity and Solution Quality Against Deme Size................. 166
17: Results for Static and Adaptive Mutation Probabilities....................................................168
18: Parallel Genetic Algorithm Parameter Specification....................................................... 189
19: RFSQP Algorithm Parameter Specification......................................................................190
20: Cluster Computing Hardware and Software Environment - Hybrid Algorithm.............. 191
21: Computation Times for the hybrid search and parallel genetic algorithms..................... 195
22: Results For Convergence Velocity, Solution Quality and Computation Time................ 199
23: Cluster Computing Hardware and Software Environment - XCS.................... ...235
24: Classification Accuracy for Burg Parametric Method (order p=6)..................................238
25: Classification Accuracy for Cov Parametric Method (order p=6)...................................238
26: Classification Accuracy for MCov Parametric Method (order p=6)................................238
27: Average Classification Results for the Different Parametric Methods (order:p=6)........ 239
LIST OF TABLES 14
28: Classification Accuracy for Frequency-Band method..................................................... 240
29: Classifier Scheme Comparison Using Parametric Methods (order p=6).........................241
30: Classifier Scheme Comparison Using Frequency-Band Representation.........................241
31: Results For Average Classification Accuracy of Parametric Methods............................242
32: Results For Average Classification Accuracy of EEG Frequency-Bands.......................243
33: Results For Average Classification Accuracy between The Parametric and Frequency-
Band Representations............................................................................................................. 246
34: Results For Average Classification Accuracy using Parametric Representation.............248
35: Results For Average Classification Accuracy using Frequency-Band Representation....248
36: Cluster Computing Hardware and Software Environment - pXCS.................................263
37: Tasks, Topological and Participatory Experiment Matrix for pXCS...............................266
Certificate of Authorship / Originality
I certify that the work in this thesis has not previously been submitted for a degree nor has it
been submitted as part of the requirements for a degree except as acknowledged within the
text.
I also certify that the thesis has been written by me. Any assistance that I have received in my
research work and the preparation of the thesis itself has been acknowledged. In addition, I
certify that all information sources and literature used are indicated in the thesis.
Signature of Candidate
Abstract
Genetic-based Learning Classifier Systems have been proposed as a competent technology for
the classification of medical data sets. What is not known about this class of system is two
fold. Firstly, how does a Learning Classifier System (LCS) perform when applied to the
single-step classification of multiple-channel, noisy, artefact-inclusive human EEG signals
acquired from many participants? Secondly and more importantly, is how the learning
classifier system performs when incorporated with migration strategies, inspired by multi-
deme, coarse-grained Parallel Genetic Algorithms (PGA) to provide parallel and distributed
classifier migration? This research investigates these open questions and concludes, subject to
the considerations herein, that these technological approaches can provide competitive
classification performance for such applications.
We performed a preliminary examination and implementation of a parallel genetic
algorithm and hybrid local search PGA using experimental methods. The parallelisation and
incorporation of classical local search methods into a genetic algorithm are well known
methods for increasing performance and we examine this. Furthermore, inspired by the
significant improvements in convergence velocity and solution quality provided by the multi-
deme, coarse-grained Parallel Genetic Algorithm, we incorporate the method into a learning
classifier system with the aim of providing parallel and distributed classifier migration. As a
result, a unique learning classifier system (pXCS) is proposed that improves classification
accuracy, achieves increased learning rates and significantly reduces the classifier population
during learning. It is compared to the extended learning Classifier System (XCS) and several
state of the art non-evolutionary classifiers in the single-step classification of noisy, artefact-
inclusive human EEG signals, derived from mental task experiments conducted using ten
human participants.
We also conclude that establishing an appropriate migration strategy is an important cause
of pXCS learning and classification performance. However, an inappropriate migration rate,
frequency or selectiomreplacement scheme can reduce performance and we document the
factors associated with this. Furthermore, we conclude that both EEG segment size and
representation both have a significant influence on classification performance. In effect,
determining an appropriate representation of the raw EEG signal is tantamount to the
classification method itself.
ABSTRACT 17
This research allows us to further explore and incorporate pXCS evolved classifiers derived
from multi-channel human EEG signals as an interface in the control of a device such as a
powered wheelchair or brain-computer interface (BCI) applications.
Statement of Contribution
Contributions to Knowledge
We have proposed and tested three research hypotheses (H3, H4, H5) related to the Genetic-
Based Machine Learning (GBML) Classifier System for the single-step classification of
multi-channel, artefact-inclusive human electroencephalographic (EEG) signals, derived from
mental task experiments. In addition we have tested two research hypotheses (HI and H2)
related to the convergence property and solution quality of a Parallel Genetic Algorithm and
Hybrid Algorithm:
• Hypothesis HI which claims ‘The convergence velocity and solution quality
achieved by a parallel genetic algorithm, with adaptive mutation is superior to that
achieved by the serial genetic algorithm on a common set of optimisation problems.”
• Hypothesis H2 which claims “The convergence velocity, solution quality and
computation time achieved by a hybrid search algorithm is superior to that achieved
by the parallel genetic algorithm on a common set of optimisation problems.”
• Hypothesis H3 which claims “A parametric representation provides superior
performance compared to the frequency-band representation for the single-step
classification of multiple-channel, artefact-inclusive human EEG data using
evolutionary and non-evolutionary classifiers.”
• Hypothesis H4 which claims “The extended learning Classifier System (XCS)
provides superior performance when compared to the current state of the art in non
evolutionary classifiers for the single-step classification of multiple-channel, artefact-
inclusive human EEG data” •
• Hypothesis H5 which claims “The incorporation of a parallel and distributed
classifier migration policy into the extended learning Classifier System (XCS)
STATEMENT OF CONTRIBUTION 19
increases classification performance for the single-step classification of multiple-
channel, artefact-inclusive human EEG data”
The implications of these findings provide, in some measure, new knowledge about the
classification of human EEG signals using parallel and distributed Genetic-Based Machine
Learning (GBML) Classifier System. In the course of examining these hypotheses, the
following contributions were also made:
• We have determined, documented and implemented a simple mechanism for the
parallel and distributed migration of classifiers in the discovery component of a
genetic-based learning classifier system, which we called pXCS. This parallel and
distributed algorithm extends Wilson’s XCS learning classifier system by introducing
a migratory pressure, with improved learning rate, improved classification accuracy
and smaller classifier population size.
• The degree-of-connectivity implicit in the fully-connected, bi-directional and uni
directional ring topologies have a significant influence upon the learning and
classification performance of pXCS. This finding extends the number of topologies
found in the literature.
• The rate and frequency of distributed classifier migrations in pXCS does have a
significant impact on the learning and classification performance. This finding is at
variance with the literature which portrays classifications performance for idealised
problems.
• A selection and replacement scheme based upon ranking of classifier fitness,
numerosity and random in pXCS can have significant impact on the learning and
classification performance. This finding extends the number of such schemes found in
the literature for idealised problems. Furthermore, we have shown that fitness bias
selection can inhibit classification performance. •
• We have provided extensive benchmarking for the classification performance of XCS
and pXCS in an observable and repeatable manner in the single-step classification of
multiple-channel, artefact-inclusive human EEG signals. This approach allowed for
the comparison of evolutionary and current state-of-the-art non-evolutionary classifier
systems, in terms of average classification accuracy and its variance.
STATEMENT OF CONTRIBUTION 20
• In terms of learning and classification performance of XCS and pXCS, in conjunction
with typical real-time constraints of similar applications we have determined:
(a) an optimal EEG segment size of 2.0-seconds,
(b) an optimal autoregressive model order of p=6 for the Burg, Covariance, and
Modified Covariance parametric methods.
• We have achieved significant improvements in classification accuracy by increasing
the ability of XCS to form generalisations in the single-step classification of multiple-
channel, artefact-inclusive human EEG signals. More specifically, establishing a
generalisation or ‘don’t care’ probability of P#=0.86 tended to provide significant
improvements in classification accuracy compared to P#=0.76.
• We have discovered that an XCS classifier population of vV=8000 did not provide
significant improvement or degradation in classification accuracy when compared
with /V=2000 and /V=4000 for parametric representations and P^O.86.
• We have developed, documented and implemented a simple hybrid search algorithm
employing an iterative global search heuristic and cascaded architecture. Our method
loosely couples our parallel genetic algorithm with a non-evolutionary search
algorithm, which based on the Reduced Feasible Sequential Quadratic Algorithm.
The hybrid algorithm achieved significant improvements in convergence velocity,
solution quality and computation time for a suite of idealised objective functions. •
• We have developed, documented and implemented a multi-deme parallel genetic
algorithm (PGA) employing an adaptive mutation function. In general, adaptive
mutation can increase convergence velocity and solution quality of a multi-deme
PGA for a suite of idealised objective functions. This finding supports the current
literature.
Related Publications
• Performance Study of a Multi-Deme Parallel Genetic Algorithm with Adaptive
Mutation, B.T. Skinner, H.T. Nguyen, D.K. Liu, International Conference on
Autonomous Robots and Agents (ICARA), pp. 88-94, New Zealand, December 2004.
• Hybrid Optimisation Method Using PGA and SQP Algorithm, B.T. Skinner, H.T.
Nguyen, D.K. Liu, IEEE Symposium on Foundations of Computational Intelligence
(FOCI), pp. 73-80, Honolulu, Hawaii, USA, April 2007.
• Classification of EEG Signals Using a Genetic-Based Machine Learning Classifier,
B.T. Skinner, H.T. Nguyen, D.K. Liu, 29th Annual International Conference of the
IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3120-3123, Lyon,
France, 2007.
• Distributed Classifier Migration in XCS for the Classification of
Electroencephalographic Signals, B.T. Skinner, H.T. Nguyen, D.K. Liu, IEEE
Congress on Evolutionary Computation (CEC), pp. 2829-2836, Singapore, 2007.
Other Publications during PhD Candidature
• Distributed Simultaneous Task Allocation and Motion Coordination of Autonomous
Vehicles Using a Parallel Computing Cluster, A.K. Kulantunga, B.T. Skinner, D.K.
Liu, H.T. Nguyen, in Robotic Welding, Intelligence and Automation, Lecture Notes
in Control and Information Sciences (Vol. 362), Springer-Verlag, 2007, pp. 409-420.
AcknowledgmentsFirstly I would like to thank my supervisor, Professor Hung Nguyen for providing me with
the opportunity to pursue postgraduate research at the University of Technology, Sydney.
Without Hung’s support, guidance and honesty I would not have been able to become a
capable research engineer. His patience and kindness continue to surprise me. I am also
grateful to Dr Dikai Liu for his guidance, encouragement and lengthy discussions on
evolutionary computation, which proved invaluable. I am indebted to him for his friendship
and company in distant places.
I would also like to thank Dr Yvonne Tran and Professor Ashley Craig for providing access
to the EEG laboratory during participant experimentation. I am thankful for their helpful
advice, ongoing support and review of my work. I am grateful to Dr Matt Gaston and Mr
Peter Brady for establishing, maintaining and supporting the UTS Engineering Linux
Computing Cluster Environment. Without their valuable help and interest my research study
would be have been more difficult. I am also thankful for the early discussions about XCS
with by Dr Alwyn Barry.
I thank my colleagues: Lesley King and Phil Taylor for the scholarly discussions about
classification learning and many other topics, which frequently took place over a beer; Daniel
Craig for his kind cooperation during the EEG participant experiments and our lengthy chats,
which I miss; Dr Shane Magrath for providing advice on the structure and presentation of this
thesis; Dr Son Nguyen for his friendly discussions; and the many people who thoughtfully
participated in the EEG recording sessions.
Perhaps the greatest credit goes to my family who have supported me throughout my life
and without which this work would not have been possible. I would like to dedicate this
research to my wonderful Melissa for her constant empathy and unwavering support
particularly when times were tough - she is a matchless partner in all adventures.
To the people who helped
