6 research outputs found
Assessing the Generalizability of a Performance Predictive Model
A key component of automated algorithm selection and configuration, which in
most cases are performed using supervised machine learning (ML) methods is a
good-performing predictive model. The predictive model uses the feature
representation of a set of problem instances as input data and predicts the
algorithm performance achieved on them. Common machine learning models struggle
to make predictions for instances with feature representations not covered by
the training data, resulting in poor generalization to unseen problems. In this
study, we propose a workflow to estimate the generalizability of a predictive
model for algorithm performance, trained on one benchmark suite to another. The
workflow has been tested by training predictive models across benchmark suites
and the results show that generalizability patterns in the landscape feature
space are reflected in the performance space.Comment: To appear at GECCO 202
Sensitivity analysis of multilayer neural networks
Dissertation (Ph.D.) -- University of Stellenbosch, 1999.ENGLISH ABSTRACT: The application of artificial neural networks to solve classification and function approximation
problems is no longer an art. Using a neural network does not simply imply the presentation
of a data set to the network and relying on the so-called "black-box" to produce - hopefully
accurate - results. Rigorous mathematical analysis now provides a much better understanding
of what is going. on inside the "black-box". The knowledge gained from these mathematical
studies allows the development of specialized tools to increase performance, robustness and
efficiency.
This thesis proposes that sensitivity analysis of the neural network output function be used
to learn more about the inner working of multilayer feedforward neural networks. New sensitivity
analysis techniques are developed to probe the knowledge embedded in the weights
of networks, and to use this knowledge within specialized sensitivity analysis algorithms to
improve generalization performance, to reduce learning and model complexity, and to improve
convergence performance.
A general mathematical model is developed which uses first order derivatives of the neural
network output function with respect to the network parameters to quantify the effect small
perturbations to these network parameters have on the output of the network. This sensitivity
analysis model is then used to develop techniques to locate and visualize decision boundaries,
and to determine which boundaries are implemented by which hidden units. The decision
boundary detection algorithm is then used to develop an active learning algorithm for classification
problems which trains only on patterns close to decision boundaries. Patterns that
convey little information about the position of boundaries are therefore not used for training.
An incremental learning algorithm for function approximation problems is also developed to incrementally grow the training set from a candidate set by adding to the training set those
patterns that convey the most information about the function to be approximated. The sensitivity
of the network output to small perturbations of the input pattern is used as measure of
pattern informativeness. Sensitivity analysis is also used to develop a network pruning algo-rithm to remove irrelevant network parameters. The significance of a parameter is quantified
as the influence small perturbations on that parameter have on the network output. Variance
analysis is employed as pruning heuristic to decide if a parameter should be removed or not.
Elaborate experimental evidence is provided to illustrate how each one of the developed
sensitivity analysis techniques addresses the objectives of improved performance, robustness
and efficiency. These results show that the different models successfully utilize the neural
network learner's current knowledge to obtain optimal architectures and to make optimal use
of the available training data.AFRIKAANSE OPSOMMING: Die toepassing van kunsmatige neurale netwerke om klassifikasie- en funksiebenaderingsprobleme
op te los, is nie meer 'n kuns nie. Die gebruik van 'n neurale netwerk impliseer nie
meer bloot die toepassing van 'n data stel op die netwerk, en die verwagting dat die "swart
boks" - hoopvol akkurate - result ate lewer nie. Omvattende wiskundige analises verskaf nou
'n baie beter begrip van wat binne die "swart boks" aangaan. Die kennis wat van hierdie
wiskundige analises gewin is, laat die ontwikkeling van gespesialiseerde hulpmiddels toe om
prestasie, robuustheid en effektiwiteit te verbeter.
Hierdie tesis stel voor dat sensitiwiteitsanalise van die neurale netwerk afvoer funksie
aangewend word om meer oor die inner werking van multi-vlak vorentoe-voer neurale netwerke
te leer. Nuwe sensitiwiteitsanalise tegnieke word ontwikkel om die kennis vervat in die gewigte
van netwerke te ondersoek, en om hierdie kennis aan te wend binne gespesialiseerde sensitiwiteitsanalise
algoritmes om sodoende veralgemeningseienskappe te verbeter, om die kompleksiteit
van leer en model kompleksiteit te verminder, en om konvergensie eienskappe te
verbeter.
'n Algemene wiskundige model is ontwikkel wat gebruik maak van die eerste orde afgeleides
van die neurale netwerk afvoer funksie met betrekkiIig tot netwerk parameters om die
effek van klein versteurings aan hierdie netwerk parameters op die afvoer van die rietwerk te
kwantifiseer. Hierdie sensitiwiteitsanalise model word dan gebruik om tegnieke te ontwikkel
om besluitnemingsgrense op te spoor en te visualiseer, en om te bepaal watter besluitnemingsgrense
word deur watter versteekte eenhede geimplementeer. Die algoritme om besluitnemingsgrense
op te spoor word dan aangewend om 'n aktiewe-leer algoritme vir klassifikasie
probleme te ontwikkel, wat leer deur gebruik te maak van slegs daardie patrone wat naby besluitnemingsgrense Ie. Gevolglik word patrone wat min inligting bevat in verband met die
ligging van besluitnemingsgrense nie vir leer aangewend nie. 'n Inkrementele leer algoritme is
ook ontwikkel vir funksiebenaderingsprobleme waarin die leerversameling inkrementeel vanuit
'n kandidaat leerversameling gegroei word deur daardie patrone by te voeg wat die meeste
inligting vervat oor die funksie wat benader word. Die sensitiwiteit van die netwerk afvoer
tot versteurings in die toevoer patroon word gebruik as 'n maatstaf van die informatiwiteit
van daardie patroon. Sensitiwiteitsanalise is ook gebruik om 'n algoritme te ontwikkel wat
irrelevante parameters van die netwerk snoei. Die belangrikheid van 'n parameter word gekwantifiseer
as die invloed wat klein versteurings in daardie parameter het op die afvoer van die
netwerk. Variansie analise word gebruik as heuristiek om te besluit of 'n parameter gesnoei
kan word al dan nie.
Omvattende eksperimentele bewyse word verskaf om te illustreer hoe elkeen van die sensitiwiteitsanalise
tegnieke wat in hierdie tesis ontwikkel is, die doelwitte van verbeterde prestasie,
robuustheid en effektiwiteit adresseer. Hierdie resultate toon aan dat die onderskeie modelle
suksesvol gebruik maak van die neurale netwerk se huidige kennis om optimale argitekture
op te stel, en om optimaal van die beskikbare leerdata gebruik te maak
An Exploratory Landscape Analysis-Based Benchmark Suite
The choice of which objective functions, or benchmark problems, should be used to test an optimization algorithm is a crucial part of the algorithm selection framework. Benchmark suites that are often used in the literature have been shown to exhibit poor coverage of the problem space. Exploratory landscape analysis can be used to quantify characteristics of objective functions. However, exploratory landscape analysis measures are based on samples of the objective function, and there is a lack of work on the appropriate choice of sample size needed to produce reliable measures. This study presents an approach to determine the minimum sample size needed to obtain robust exploratory landscape analysis measures. Based on reliable exploratory landscape analysis measures, a self-organizing feature map is used to cluster a comprehensive set of benchmark functions. From this, a benchmark suite that has better coverage of the single-objective, boundary-constrained problem space is proposed
An Exploratory Landscape Analysis-Based Benchmark Suite
The choice of which objective functions, or benchmark problems, should be used to test an optimization algorithm is a crucial part of the algorithm selection framework. Benchmark suites that are often used in the literature have been shown to exhibit poor coverage of the problem space. Exploratory landscape analysis can be used to quantify characteristics of objective functions. However, exploratory landscape analysis measures are based on samples of the objective function, and there is a lack of work on the appropriate choice of sample size needed to produce reliable measures. This study presents an approach to determine the minimum sample size needed to obtain robust exploratory landscape analysis measures. Based on reliable exploratory landscape analysis measures, a self-organizing feature map is used to cluster a comprehensive set of benchmark functions. From this, a benchmark suite that has better coverage of the single-objective, boundary-constrained problem space is proposed
Function minimization in DNA sequence design based on continuous particle swarm optimization
In DNA based computation and DNA nanotechnology, the design of good DNA sequences has turned out to be an essential problem and one of the most practical and important research topics. Basically, the DNA sequence design problem is a multi-objective problem, and it can be evaluated using four objective functions, namely, Hmeasurei similarity, continuity, and hairpin. In this paper, particle swarm optimization (PSO) is proposed to minimize those objective functions, individually, subjected to two constraints: melting temperature, Tm, and GCcontent. A model is presented in order to minimize the objective functions using PSO. An implementation of the optimization process is presented using 20 particles. The results obtained verified that PSO can be used to minimize each objective individually