%EuroGP 2002 bibtex file W B Langdon 19 February 2002
%Data supplied 10 (12) February 2002 by Andrea Tettamanzi

@proceedings (lutton:2002:GP,
  title =	{Proceedings of the 4th European Conference on Genetic Programming, EuroGP 2002},
  year =	2002,
  editor =	{Evelyne Lutton and James A. Foster and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi},
  volume =	{2278},
  series =	{LNCS},
  address =	{Kinsale, Ireland},
  publisher_address = 	{Berlin},
  month = 	{3-5 April},
  organisation ={EvoNet},
  publisher = 	{Springer-Verlag},
  note =	{},
  note =	{},
% email =	{},
  keywords =    {genetic algorithms, genetic programming},
  ISBN =        {},
  url =         {},
  size =	{336 pages},
  abstract =	{},
  notes =	{EuroGP'2002}
)


@inproceedings(martin:2002:EuroGP,
    title = {A Pipelined Hardware Implementation of Genetic Programming using {FPGA}s and {Handel-C}},
    author = {Peter Martin},
    editor = {Evelyne Lutton and James A. Foster and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi},
    booktitle = {Proceedings of the 4th European Conference on Genetic Programming, EuroGP 2002},
    volume =	{2278},
    series = 	{LNCS},
    pages = {1--15},
    publisher = {Springer-Verlag},
    address = {Kinsale, Ireland},
    publisher_address = 	{Berlin},
    month = 	{3-5 April},
    year = 2002,
    keywords = 	{genetic algorithms, genetic programming},
    abstract = 	{A complete Genetic Programming (GP) system implemented in a single FPGA is
described in this paper. The GP system is capable of solving problems that
require large populations and by using parallel fitness evaluations can
solve problems in a much shorter time that a conventional GP system in
software. A high level language to hardware compilation system called
Handel-C is used for implementation.
},
    notes = 	{EuroGP'2002, part of lutton:2002:GP},
)

@inproceedings(yu:2002:EuroGP,
    title = {Needles in Haystacks Are Not Hard to Find with Neutrality},
    author = {Tina Yu and Julian Miller},
    editor = {Evelyne Lutton and James A. Foster and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi},
    booktitle = {Proceedings of the 4th European Conference on Genetic Programming, EuroGP 2002},
    volume =	{2278},
    series = 	{LNCS},
    pages = {16--27},
    publisher = {Springer-Verlag},
    address = {Kinsale, Ireland},
    publisher_address = 	{Berlin},
    month = 	{3-5 April},
    year = 2002,
    keywords = 	{genetic algorithms, genetic programming},
    abstract = 	{We propose building neutral networks in needle-in-haystack fitness landscapes
to assist an evolutionary algorithm to perform search. The experimental results
on four different problems show that this approach improves the search success
rates in most cases. In situations where neutral networks do not give
performance improvement, no impairment occurs either. We also tested a
hypothesis proposed in our previous work. The results support the hypothesis:
when the ratio of adaptive/neutral mutations during neutral walk is close to
that of fitness improvement step, the evolutionary search has a high success
rate. Moreover, the ratio magnitudes indicate that more neutral mutations (than
adaptive mutations) are required for the algorithms to find a solution in this
type of search space.
},
    notes = 	{EuroGP'2002, part of lutton:2002:GP},
)

@inproceedings(streeter:2002:EuroGP,
    title = {Routine Duplication of Post-2000 Patented Inventions by Means of Genetic Programming},
    author = {Matthew J. Streeter and Martin A. Keane and John R. Koza},
    editor = {Evelyne Lutton and James A. Foster and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi},
    booktitle = {Proceedings of the 4th European Conference on Genetic Programming, EuroGP 2002},
    publisher = {Springer-Verlag},
    volume =	{2278},
    series = 	{LNCS},
    pages = {28--37},
    address = {Kinsale, Ireland},
    publisher_address = 	{Berlin},
    month = 	{3-5 April},
    year = 2002,
    keywords = 	{genetic algorithms, genetic programming},
    abstract = 	{Previous work has demonstrated that genetic programming can automatically
create analog electrical circuits, controllers, and other devices that
duplicate the functionality and, in some cases, partially or completely
duplicate the exact structure of inventions that were patented between 1917 and
1962. This paper reports on a project in which we browsed patents of analog
circuits issued after January 1, 2000 on the premise that recently issued
patents represent current research that is considered to be of practical and
scientific importance. The paper describes how we used genetic programming to
automatically create circuits that duplicate the functionality or structure of
five post -2000 patented inventions. This work employed four new techniques
(motivated by the theory of genetic algorithms and genetic programming) that
we believe increased the efficiency of the runs. When an automated method
duplicates a previously patented human -designed invention, it can be argued
that the automated method satisfies a Patent -Office-based variation of the
Turing test.
},
    notes = 	{EuroGP'2002, part of lutton:2002:GP},
)

@inproceedings(brameier:2002:EuroGP,
    title = {Explicit Control of Diversity and Effective Variation Distance in Linear Genetic Programming},
    author = {Markus Brameier and Wolfgang Banzhaf},
    editor = {Evelyne Lutton and James A. Foster and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi},
    booktitle = {Proceedings of the 4th European Conference on Genetic Programming, EuroGP 2002},
    publisher = {Springer-Verlag},
    volume =	{2278},
    series = 	{LNCS},
    pages = {38--50},
    address = {Kinsale, Ireland},
    publisher_address = 	{Berlin},
    month = 	{3-5 April},
    year = 2002,
    keywords = 	{genetic algorithms, genetic programming},
    abstract = 	{We have investigated structural distance metrics for linear genetic programs.
Causal connections between changes of the genotype and changes of the phenotype
form a necessary condition for analyzing structural differences between genetic
programs and for the two objectives of this paper: (i) The distance information
between individuals is used to control structural diversity of population
individuals actively by a two-level tournament selection. (ii) Variation
distance of effective code is controlled for different genetic operators -
including a mutation operator that works closely with the applied distance
measure. Numerous experiments have been performed for three benchmark problems.
},
    notes = 	{EuroGP'2002, part of lutton:2002:GP},
)

@inproceedings(ferreira:2002:EuroGP,
    title = {Discovery of the Boolean Functions to the Best Density-Classification Rules Using Gene Expression Programming},
    author = {C\^andida Ferreira},
    editor = {Evelyne Lutton and James A. Foster and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi},
    booktitle = {Proceedings of the 4th European Conference on Genetic Programming, EuroGP 2002},
    volume =	{2278},
    series = 	{LNCS},
    pages = {51--60},
    publisher = {Springer-Verlag},
    address = {Kinsale, Ireland},
    publisher_address = 	{Berlin},
    month = 	{3-5 April},
    year = 2002,
    keywords = 	{genetic algorithms, genetic programming},
    abstract = 	{Cellular automata are idealized versions of massively parallel, decentralized
computing systems capable of emergent behaviours. These complex behaviors result
from the simultaneous execution of simple rules at multiple local sites. A
widely studied behavior consists of correctly determining the density of an
initial configuration, and both human and computer-written rules have been
found that perform with high efficiency at this task. However, the two best
rules for the density-classification task, Coevolution1 and Coevolution2, were
discovered using a coevolutionary algorithm in which a genetic algorithm
evolved the rules and, therefore, only the output bits of the rules are known.
However, to understand why these and other rules perform so well and how the
information is transmitted throughout the cellular automata, the Boolean
expressions that orchestrate this behaviour must be known. The results presented
in this work are a contribution in that direction.
},
    notes = 	{EuroGP'2002, part of lutton:2002:GP},
)

@inproceedings(langdon:2002:EuroGP,
    title = {Combining Decision Trees and Neural Networks for Drug Discovery},
    author = {William B. Langdon and S. J. Barrett and B. F. Buxton},
    editor = {Evelyne Lutton and James A. Foster and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi},
    booktitle = {Proceedings of the 4th European Conference on Genetic Programming, EuroGP 2002},
    volume =	{2278},
    series = 	{LNCS},
    pages = {61--70},
    publisher = {Springer-Verlag},
    address = {Kinsale, Ireland},
    publisher_address = 	{Berlin},
    month = 	{3-5 April},
    year = 2002,
    keywords = 	{genetic algorithms, genetic programming},
    abstract = 	{Genetic programming (GP) offers a generic method of 
automatically fusing together
classifiers 
using their receiver operating characteristics (ROC)
to yield superior ensembles.
We combine decision trees (C4.5) and 
artificial neural networks (ANN)
on a difficult pharmaceutical
data mining (KDD)
drug discovery application.
Specifically predicting inhibition of a P450 enzyme.
Training data came from
high throughput screening (HTS) runs.
The evolved model may be used to predict
behaviour of virtual (i.e.
yet to be manufactured) chemicals.
Measures to reduce over fitting are also described.
},
    notes = 	{EuroGP'2002, part of lutton:2002:GP},
)

@inproceedings(eggermont:2002:EuroGP,
    title = {Evolving Fuzzy Decision Trees with Genetic Programming and Clustering},
    author = {Jeroen Eggermont},
    editor = {Evelyne Lutton and James A. Foster and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi},
    booktitle = {Proceedings of the 4th European Conference on Genetic Programming, EuroGP 2002},
    volume =	{2278},
    series = 	{LNCS},
    pages = {71--82},
    publisher = {Springer-Verlag},
    address = {Kinsale, Ireland},
    publisher_address = 	{Berlin},
    month = 	{3-5 April},
    year = 2002,
    keywords = 	{genetic algorithms, genetic programming},
    abstract = 	{In this paper we present a new fuzzy decision tree representation for
n-category data classification using genetic programming. The new fuzzy
representation uses fuzzy clusters for handling continuous attributes. To
make optimal use of the fuzzy classifications of this representation an extra
fitness measure is used. The new fuzzy representation will be compared, using
several machine learning data sets, to a similar non-fuzzy representation as
well as to some other evolutionary and non-evolutionary algorithms from
literature.
},
    notes = 	{EuroGP'2002, part of lutton:2002:GP},
)

@inproceedings(kantschik:2002:EuroGP,
    title = {Linear-Graph {GP}---A new {GP} Structure},
    author = {Wolfgang Kantschik and Wolfgang Banzhaf},
    editor = {Evelyne Lutton and James A. Foster and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi},
    booktitle = {Proceedings of the 4th European Conference on Genetic Programming, EuroGP 2002},
    volume =	{2278},
    series = 	{LNCS},
    pages = {83--92},
    publisher = {Springer-Verlag},
    address = {Kinsale, Ireland},
    publisher_address = 	{Berlin},
    month = 	{3-5 April},
    year = 2002,
    keywords = 	{genetic algorithms, genetic programming},
    abstract = 	{In recent years different genetic programming (GP) structures have emerged.
Today, the basic forms of representation for genetic programs are tree, linear
and graph structures. In this contribution we introduce a new kind of GP
structure which we call linear-graph, it is a further development of the
linear-Tree structure. We describe the linear-graph structure, as well as
crossover and mutation for this new GP structure in detail. We compare
linear-graph programs with linear and tree programs by analyzing their
structure and results on different test problems.
},
    notes = 	{EuroGP'2002, part of lutton:2002:GP},
)

@inproceedings(weinert:2002:EuroGP,
    title = {Parallel Surface Reconstruction},
    author = {Klaus Weinert and Tobias Surmann and J\"orn Mehnen},
    editor = {Evelyne Lutton and James A. Foster and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi},
    booktitle = {Proceedings of the 4th European Conference on Genetic Programming, EuroGP 2002},
    volume =	{2278},
    series = 	{LNCS},
    pages = {93--102},
    publisher = {Springer-Verlag},
    address = {Kinsale, Ireland},
    publisher_address = 	{Berlin},
    month = 	{3-5 April},
    year = 2002,
    keywords = 	{genetic algorithms, genetic programming},
    abstract = 	{The task of surface reconstruction is to find a mathematical
representation of a surface which is given only by a set of
discrete sampling points. The mathematical description in the
computer allows to save or transfer the geometric data via internet,
to manipulate (e.g. for aerodynamic or design specific reasons)
or to optimize the machining of the work pieces. The reconstruction
of the shape of an object is a difficult mathematical and
computer scientific problem. For this reason a GP/ES-hybrid algorithm has been
used. Due to the high complexity of the problem and in order to speed up
the reconstruction process, the algorithm has been enhanced to work
as a multipopulation GP/ES that runs in parallel on a network of
standard PCs.
},
    notes = 	{EuroGP'2002, part of lutton:2002:GP},
)

@inproceedings(brabazon:2002:EuroGP,
    title = {Evolving classifiers to model the relationship between strategy and corporate performance using grammatical evolution},
    author = {Anthony Brabazon and Michael O'Neill and Conor Ryan and Robin Matthews},
    editor = {Evelyne Lutton and James A. Foster and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi},
    booktitle = {Proceedings of the 4th European Conference on Genetic Programming, EuroGP 2002},
    volume =	{2278},
    series = 	{LNCS},
    pages = {103--112},
    address = {Kinsale, Ireland},
    publisher_address = 	{Berlin},
    month = 	{3-5 April},
    publisher = {Springer-Verlag},
    year = 2002,
    keywords = 	{genetic algorithms, genetic programming},
    abstract = 	{This study examines the potential of grammatical evolution to construct a 
linear classifier to predict whether a firm's corporate strategy will 
increase or decrease shareholder wealth.
Shareholder wealth is measured using a relative fitness criterion, the 
change in a firm's market-value-added ranking in the Stern-Stewart 
Performance 1000 list, over a four year period, 1992-1996.
Model inputs and structure are selected by means of grammatical evolution. 
The best classifier correctly categorised the direction
of performance ranking change in 66.38% of the firms in the training set 
and 65% in the out-of-sample validation set providing support for a 
hypothesis that changes in corporate strategy are linked to changes in 
corporate performance.
},
    notes = 	{EuroGP'2002, part of lutton:2002:GP},
)

@inproceedings(weinert:2002:EuroGPa,
    title = {A New View on Symbolic Regression},
    author = {Klaus Weinert and Marc Stautner},
    editor = {Evelyne Lutton and James A. Foster and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi},
    booktitle = {Proceedings of the 4th European Conference on Genetic Programming, EuroGP 2002},
    volume =	{2278},
    series = 	{LNCS},
    pages = {113--122},
    publisher = {Springer-Verlag},
    address = {Kinsale, Ireland},
    publisher_address = 	{Berlin},
    month = 	{3-5 April},
    year = 2002,
    keywords = 	{genetic algorithms, genetic programming},
    abstract = 	{Symbolic regression is a widely used method to reconstruct mathematical
correlations. This paper presents a new graphical representation of the
individuals reconstructed in this process. This new three dimensional
representation allows the user to  recognize certain possibilities to
improve his setup of the process parameters. Furthermore this new
representation allows a wider usage of the generated three dimensional
objects with nearly every CAD program for further use.  To show the
practical usage of this new representation it was used to reconstruct
mathematical descriptions of the dynamics in a machining process namely in
orthogonal cutting.
},
    notes = 	{EuroGP'2002, part of lutton:2002:GP},
)

@inproceedings(keijzer:2002:EuroGP,
    title = {Grammatical Evolution Rules: The mod and the Bucket Rule},
    author = {Maarten Keijzer and Michael O'Neill and Conor Ryan and Mike Cattolico},
    editor = {Evelyne Lutton and James A. Foster and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi},
    booktitle = {Proceedings of the 4th European Conference on Genetic Programming, EuroGP 2002},
    volume =	{2278},
    series = 	{LNCS},
    pages = {123--131},
    publisher = {Springer-Verlag},
    address = {Kinsale, Ireland},
    publisher_address = 	{Berlin},
    month = 	{3-5 April},
    year = 2002,
    keywords = 	{genetic algorithms, genetic programming, gramatical evolution},
    abstract = 	{We present an alternative mapping function called the Bucket Rule, for
Grammatical Evolution, that improves upon the standard modulo rule. Grammatical
Evolution is applied to a set of standard Genetic Algorithm problem domains
using two alternative grammars. Applying GE to GA problems allows us to focus
on a simple grammar whose effects are easily analysable.
},
    notes = 	{EuroGP'2002, part of lutton:2002:GP},
)

@inproceedings(ryan:2002:EuroGP,
    title = {No Coercion and No Prohibition, A Position Independent Encoding Scheme for Evolutionary Algorithms---The {Chorus} System},
    author = {Conor Ryan and Atif Azad and Alan Sheahan and Michael O'Neill},
    editor = {Evelyne Lutton and James A. Foster and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi},
    booktitle = {Proceedings of the 4th European Conference on Genetic Programming, EuroGP 2002},
    volume =	{2278},
    series = 	{LNCS},
    pages = {132--142},
    publisher = {Springer-Verlag},
    address = {Kinsale, Ireland},
    publisher_address = 	{Berlin},
    month = 	{3-5 April},
    year = 2002,
    keywords = 	{genetic algorithms, genetic programming},
    abstract = 	{We describe a new encoding system, Chorus, for grammar based Evolutionary
Algorithms. This scheme is coarsely based on the manner in nature in which
genes produce proteins that regulate the metabolic pathways of the cell. The
phenotype is the behaviour of the cells metabolism,  which corresponds to the
development of the computer program in our  case. In this procedure, the actual
protein encoded by a gene is the same regardless of the position of the gene
within the genome.

We show that the Chorus system has a very convenient Regular Expression - type
schema notation that can be used to describe the presence of various phenotypes
or phenotypic traits. This schema notation is used to demonstrate that massive
areas of neutrality can exist in the search landscape, and the system is also
shown to be able to dispense with large areas of the search space that are
unlikely to contain useful solutions.
},
    notes = 	{EuroGP'2002, part of lutton:2002:GP},
)

@inproceedings(soule:2002:EuroGP,
    title = {Exons and Code Growth in Genetic Programming},
    author = {Terence Soule},
    editor = {Evelyne Lutton and James A. Foster and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi},
    booktitle = {Proceedings of the 4th European Conference on Genetic Programming, EuroGP 2002},
    volume =	{2278},
    series = 	{LNCS},
    pages = {143--152},
    publisher = {Springer-Verlag},
    address = {Kinsale, Ireland},
    publisher_address = 	{Berlin},
    month = 	{3-5 April},
    year = 2002,
    keywords = 	{genetic algorithms, genetic programming},
    abstract = 	{The phenomenon of code growth is well documented in genetic programming.
Several well supported theories exist to explain code growth, each of which
focuses on introns, sections of code that do not contribute to fitness.
However, several researchers have pointed out that these theories, and code
growth itself, does not seem to depend upon the presence of introns. In this
paper we show for the first time that code growth can occur, albeit quite
slowly, even with exons that have a significant impact on fitness.
},
    notes = 	{EuroGP'2002, part of lutton:2002:GP},
)

@inproceedings(vanbelle:2002:EuroGP,
    title = {Uniform Subtree Mutation},
    author = {Terry {Van Belle} and David H. Ackley},
    editor = {Evelyne Lutton and James A. Foster and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi},
    booktitle = {Proceedings of the 4th European Conference on Genetic Programming, EuroGP 2002},
    volume =	{2278},
    series = 	{LNCS},
    pages = {153--162},
    publisher = {Springer-Verlag},
    address = {Kinsale, Ireland},
    publisher_address = 	{Berlin},
    month = 	{3-5 April},
    year = 2002,
    keywords = 	{genetic algorithms, genetic programming},
    abstract = 	{Genetic programming methods often suffer from `code bloat,' in which
evolving solution trees rapidly become unmanageably large.  To provide
a measure of sensitivity to tree size in a natural way, we introduce
a simple uniform subtree mutation (USM) operator that provides an
approximately constant probability of mutation per tree node, rather than
per tree.  To help model circumstances where tree size cannot be ignored,
we introduce a new notion of computational effort called size effort.
Initial empirical tests show that genetic programming using only uniform
subtree mutation reduces evolved tree sizes dramatically, compared to
crossover, but does impact solution quality somewhat.  In some cases,
however, using using a combination of USM and crossover yielded both
smaller trees and superior performance, as measured both by size effort
and traditional metrics.
},
    notes = 	{EuroGP'2002, part of lutton:2002:GP},
)

@inproceedings(ekart:2002:EuroGP,
    title = {Maintaining the Diversity of Genetic Programs},
    author = {Anik\'o Ek\'art and Sandor Zoltan N\'emeth},
    editor = {Evelyne Lutton and James A. Foster and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi},
    booktitle = {Proceedings of the 4th European Conference on Genetic Programming, EuroGP 2002},
    volume =	{2278},
    series = 	{LNCS},
    pages = {163--172},
    publisher = {Springer-Verlag},
    address = {Kinsale, Ireland},
    publisher_address = 	{Berlin},
    month = 	{3-5 April},
    year = 2002,
    keywords = 	{genetic algorithms, genetic programming},
    abstract = 	{An important problem of evolutionary algorithms is that throughout
evolution they loose genetic diversity. Many techniques have been
developed for maintaining diversity in genetic algorithms, but few
investigations have been done for genetic programs. We define here a
diversity measure for genetic programs based on our metric for genetic
trees. We use this distance measure for studying the effects of fitness
sharing. We then propose a method for adaptively maintaining the
diversity of a population during evolution.
},
    notes = 	{EuroGP'2002, part of lutton:2002:GP},
)

@inproceedings(imamura:2002:EuroGP,
    title = {{$N$}-version Genetic Programming via Fault Masking},
    author = {Kosuke Imamura and Robert B. Heckendorn and Terence Soule and James A. Foster},
    editor = {Evelyne Lutton and James A. Foster and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi},
    booktitle = {Proceedings of the 4th European Conference on Genetic Programming, EuroGP 2002},
    volume =	{2278},
    series = 	{LNCS},
    pages = {173--182},
    publisher = {Springer-Verlag},
    address = {Kinsale, Ireland},
    publisher_address = 	{Berlin},
    month = 	{3-5 April},
    year = 2002,
    keywords = 	{genetic algorithms, genetic programming},
    abstract = 	{We introduce a new method, N-Version Genetic Programming (NVGP), for building
fault tolerant software by building an ensemble of automatically generated
modules in such a way as to maximize their collective fault masking ability.
The ensemble itself is an example of n-version modular redundancy for fault
tolerance, where the output of the ensemble is the most frequent output of n
independent modules. By maximising collective fault masking, NVGP approaches
the fault tolerance expected from n version modular redundancy with independent
faults in component modules. The ensemble comprises individual modules from a
large pool generated with genetic programming, using operators that increase
the diversity of the population. Our experimental test problem classified
promoter regions in Escherichia coli DNA sequences. For this problem, NVGP
reduced the number and variance of errors over single modules produced by GP,
with statistical significance.
},
    notes = 	{EuroGP'2002, part of lutton:2002:GP},
)

@inproceedings(christensen:2002:EuroGP,
    title = {An Analysis of {Koza}'s Computational Effort Statistic for Genetic Programming},
    author = {Steffen Christensen and Franz Oppacher},
    editor = {Evelyne Lutton and James A. Foster and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi},
    booktitle = {Proceedings of the 4th European Conference on Genetic Programming, EuroGP 2002},
    volume =	{2278},
    series = 	{LNCS},
    pages = {183--192},
    publisher = {Springer-Verlag},
    address = {Kinsale, Ireland},
    publisher_address = 	{Berlin},
    month = 	{3-5 April},
    year = 2002,
    keywords = 	{genetic algorithms, genetic programming},
    abstract = 	{As research into the theory of genetic programming progresses, more effort is
being placed on systematically comparing results to give an indication of the
effectiveness of sundry modifications to traditional GP. The statistic that is
commonly used to report the amount of computational effort to solve a
particular problem with 99% probability is Koza's I(M, i, z) statistic.
This paper analyzes this measure from a statistical perspective. In
particular, Koza's I tends to underestimate the true computational effort, by
25% or more for commonly used GP parameters and run sizes.  The magnitude of
this underestimate is nonlinearly decreasing with increasing run count, leading
to the possibility that published results based on few runs may in fact be
unmatchable when replicated at higher resolution. Additional analysis shows
that this statistic also under reports the generation at which optimal results
are achieved.  
},
    notes = 	{EuroGP'2002, part of lutton:2002:GP},
)

% Papers accepted for a Poster

@inproceedings(werner:2002:EuroGP,
    title = {Genetic control applied to asset managements},
    author = {James Cunha Werner and Terence C. Fogarty},
    editor = {Evelyne Lutton and James A. Foster and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi},
    booktitle = {Proceedings of the 4th European Conference on Genetic Programming, EuroGP 2002},
    volume =	{2278},
    series = 	{LNCS},
    pages = {193--202},
    publisher = {Springer-Verlag},
    address = {Kinsale, Ireland},
    publisher_address = 	{Berlin},
    month = 	{3-5 April},
    year = 2002,
    keywords = 	{genetic algorithms, genetic programming},
    abstract = 	{This paper address the problem of investment optimisation, with deals
with obtain stock time series from data extracted of graphics
available in internet, predict assets price by adaptive algorithms,
optimise the portfolio with genetic algorithms and obtain a recursive
model of portfolio composition on-fly using genetic programming, all
steps integrated to obtain an automatic management. The final result
is a real-time update portfolio composition for each asset.
},
    notes = 	{EuroGP'2002, part of lutton:2002:GP},
)

@inproceedings(baber:2002:EuroGP,
    title = {Evolutionary Algorithm Approach to Bilateral Negotiations},
    author = {Vinaysheel Baber and Rema Ananthanarayanan and Krishna Kummamuru},
    editor = {Evelyne Lutton and James A. Foster and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi},
    booktitle = {Proceedings of the 4th European Conference on Genetic Programming, EuroGP 2002},
    volume =	{2278},
    series = 	{LNCS},
    pages = {203--212},
    publisher = {Springer-Verlag},
    address = {Kinsale, Ireland},
    publisher_address = 	{Berlin},
    month = 	{3-5 April},
    year = 2002,
    keywords = 	{genetic algorithms, genetic programming},
    abstract = 	{The Internet is quickly changing the way business-to-consumer and
business-to-business commerce is conducted. The technology has created an
opportunity to get beyond single-issue negotiation by determining sellers' and
buyers' preferences across multiple issues, thereby creating possible joint
gains for all parties. We develop simple multiple issue algorithms and
heuristics that could be used in electronic auctions and electronic markets. In
this study, we show how a genetic algorithm based technique, coupled with a
simple heuristic can achieve good results in business negotiations. The
negotiations' outcomes are evaluated on two dimensions: joint utility and
number of ex-changes of offers to reach a deal. The results are promising and
indicate possible use of such approaches in actual electronic commerce
systems.
},
    notes = 	{EuroGP'2002, part of lutton:2002:GP},
)

@inproceedings(poli:2002:EuroGP,
    title = {Allele Diffusion in Linear Genetic Programming and Variable-Length Genetic Algorithms with Subtree Crossover},
    author = {Riccardo Poli and Jonathan E. Rowe and Christopher R. Stephens and Alden H. Wright},
    editor = {Evelyne Lutton and James A. Foster and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi},
    booktitle = {Proceedings of the 4th European Conference on Genetic Programming, EuroGP 2002},
    volume =	{2278},
    series = 	{LNCS},
    pages = {213--228},
    publisher = {Springer-Verlag},
    address = {Kinsale, Ireland},
    publisher_address = 	{Berlin},
    month = 	{3-5 April},
    year = 2002,
    keywords = 	{genetic algorithms, genetic programming},
    abstract = 	{In this paper we study, theoretically, the search biases produced by GP subtree
crossover when applied to linear representations, such as those used in linear
GP or in variable length GAs. The study naturally leads to generalisations of
Geiringer s theorem and of the notion of linkage equilibrium, which, until now,
were applicable only to fixed-length representations. This indicates the
presence of a diffusion process by which, even in the absence of selective
pressure and mutation, the alleles in a particular individual tend not just to
be swapped with those of other individuals in the population, but also to
diffuse within the representation of each individual. More precisely, crossover
attempts to push the population towards distributions of primitives where each
primitive is equally likely to be found in any position in any individual.
},
    notes = 	{EuroGP'2002, part of lutton:2002:GP},
)

@inproceedings(hoai:2002:EuroGP,
    title = {Some Experimental Results with Tree Adjunct Grammar Guided Genetic Programming},
    author = {Nguyen Xuan Hoai and R. I. McKay and D. Essam},
    editor = {Evelyne Lutton and James A. Foster and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi},
    booktitle = {Proceedings of the 4th European Conference on Genetic Programming, EuroGP 2002},
    volume =	{2278},
    series = 	{LNCS},
    pages = {229--238},
    publisher = {Springer-Verlag},
    address = {Kinsale, Ireland},
    publisher_address = 	{Berlin},
    month = 	{3-5 April},
    year = 2002,
    keywords = 	{genetic algorithms, genetic programming},
    abstract = 	{Tree-adjunct grammar guided genetic programming (TAG3P) [5] is a grammar guided
genetic programming system that uses context -free grammars along with
tree-adjunct grammars as means to set language bias for the genetic programming
system. In this paper, we show the experimental results of  TAG3P on two
problems: symbolic regression and trigonometric identity discovery. The results
show that TAG3P works well on those problems.
},
    notes = 	{EuroGP'2002, part of lutton:2002:GP},
)

@inproceedings(gustafson:2002:EuroGP,
    title = {A Puzzle to Challenge Genetic Programming},
    author = {Edmund Burke and Steven Gustafson and Graham Kendall},
    editor = {Evelyne Lutton and James A. Foster and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi},
    booktitle = {Proceedings of the 4th European Conference on Genetic Programming, EuroGP 2002},
    volume =	{2278},
    series = 	{LNCS},
    pages = {239--248},
    publisher = {Springer-Verlag},
    address = {Kinsale, Ireland},
    publisher_address = 	{Berlin},
    month = 	{3-5 April},
    year = 2002,
    keywords = 	{genetic algorithms, genetic programming},
    abstract = 	{This report represents an initial investigation into the use of genetic  
programming to solve the N-prisoners puzzle. The puzzle has generated a 
certain level of interest among the mathematical community. We believe that 
this puzzle presents a significant challenge to the field of evolutionary 
computation and to genetic programming in particular. The overall aim is to 
generate a solution that encodes complex decision making. Our initial results 
demonstrate that genetic programming can evolve good solutions. We compare 
these results to engineered solutions and discuss some of the implications. 
One of the consequences of this study is that it has highlighted a number of 
research issues and directions and challenges for the evolutionary 
computation community.We conclude the article by presenting some of these 
directions which range over several areas of evolutionary computation,  
including multi-objective fitness, coevolution and cooperation, and problem 
representations. 
},
    notes = 	{EuroGP'2002, part of lutton:2002:GP},
)

@inproceedings(ibarra:2002:EuroGP,
    title = {Transformation of Equational Specification by Means of Genetic Programming},
    author = {Aitor Ibarra and J. Lanchares and J. Mendias and J. I. Hidalgo and R. Hermida},
    editor = {Evelyne Lutton and James A. Foster and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi},
    booktitle = {Proceedings of the 4th European Conference on Genetic Programming, EuroGP 2002},
    volume =	{2278},
    series = 	{LNCS},
    pages = {249--258},
    publisher = {Springer-Verlag},
    address = {Kinsale, Ireland},
    publisher_address = 	{Berlin},
    month = 	{3-5 April},
    year = 2002,
    keywords = 	{genetic algorithms, genetic programming},
    abstract = 	{High Level Synthesis (HLS) is a designing methodology aimed to the
synthesis of hardware devices from behavioural specifications. One of
the techniques used in HLS is formal verification. In this work we
present an evolutionary algorithm in order to optimize circuit
equational specifications by means of a special type of genetic
operator. We have named this operator algebraic mutation, carried out
with the help of the equations that Formal Verification Synthesis
offers. This work can be classified within the development of an
automatic tool of Formal Verification Synthesis by using genetic
techniques. We have applied this technique to a simple circuit
equational specification and to a much more complex algebraic
equation. In the first case our algorithm simplifies the equation
until the best specification is found and in the second a solution
improving the former is always obtained.
},
    notes = 	{EuroGP'2002, part of lutton:2002:GP},
)

@inproceedings(busch:2002:EuroGP,
    title = {Automatic Generation of Control Programs for Walking Robots Using Genetic Programming},
    author = {Jens Busch and Jens Ziegler and Wolfgang Banzhaf and Andree Ross and Daniel Sawitzki and Christian Aue},
    editor = {Evelyne Lutton and James A. Foster and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi},
    booktitle = {Proceedings of the 4th European Conference on Genetic Programming, EuroGP 2002},
    volume =	{2278},
    series = 	{LNCS},
    pages = {259--268},
    publisher = {Springer-Verlag},
    address = {Kinsale, Ireland},
    publisher_address = 	{Berlin},
    month = 	{3-5 April},
    year = 2002,
    keywords = 	{genetic algorithms, genetic programming},
    abstract = 	{We present the system SIGEL that combines the simulation and visualization of
robots with a Genetic Programming system for the automated evolution of
walking. It is designed to automatically generate control programs for
arbitrary robots without depending on detailed analytical information of the
robots' kinematic structure. Different fitness functions as well as a variety
of parameters allow the easy and interactive configuration and adaptation of
the evolution process and the simulations.
},
    notes = 	{EuroGP'2002, part of lutton:2002:GP},
)

@inproceedings(osullivan:2002:EuroGP,
    title = {An investigation into the use of different search strategies with Grammatical Evolution},
    author = {John O'Sullivan and Conor Ryan},
    editor = {Evelyne Lutton and James A. Foster and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi},
    booktitle = {Proceedings of the 4th European Conference on Genetic Programming, EuroGP 2002},
    volume =	{2278},
    series = 	{LNCS},
    pages = {269--278},
    publisher = {Springer-Verlag},
    address = {Kinsale, Ireland},
    publisher_address = 	{Berlin},
    month = 	{3-5 April},
    year = 2002,
    keywords = 	{genetic algorithms, genetic programming},
    abstract = 	{We present an investigation into the performance of Grammatical
Evolution using a number of different search strategies, Simulated
Annealing, Hill Climbing, Random Search and Genetic
Algorithms. Comparative results on three different problems are
examined. We analyse the nature of the search spaces presented by
these problems and offer an explanation for the contrasting
performance of each of the search strategies. Our results show that
Genetic Algorithms provide a consistent level of performance across
all three problems successfully coping with sensitivity of the system
to discrete changes in the selection of productions from the
associated grammar.
},
    notes = 	{EuroGP'2002, part of lutton:2002:GP},
)

@inproceedings(ryan:2002:EuroGPa,
    title = {Genetic Algorithms Using Grammatical Evolution},
    author = {Conor Ryan and Miguel Nicolau and Michael O'Neill},
    editor = {Evelyne Lutton and James A. Foster and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi},
    booktitle = {Proceedings of the 4th European Conference on Genetic Programming, EuroGP 2002},
    volume =	{2278},
    series = 	{LNCS},
    pages = {279--288},
    publisher = {Springer-Verlag},
    address = {Kinsale, Ireland},
    publisher_address = 	{Berlin},
    month = 	{3-5 April},
    year = 2002,
    keywords = 	{genetic algorithms, genetic programming},
    abstract = {This paper describes the GAUGE system, Genetic
    Algorithms Using Grammatical Evolution. GAUGE is a position
    independent Genetic Algorithm that uses Grammatical Evolution with
    an attribute grammar to dictate what position a gene codes
    for. GAUGE suffers from neither under-specification nor
    over-specification, is guaranteed to produce syntactically correct
    individuals, and does not require any repair after the application
    of genetic operators. GAUGE is applied to the standard onemax
    problem, with results showing that its genotype to phenotype
    mapping and position independence nature do not affect its
    performance as a normal genetic algorithm. A new problem is also
    presented, a deceptive version of the Mastermind game, and we show
    that GAUGE possesses the position independence characteristics it
    claims, and outperforms several genetic algorithms, including the
    competent genetic algorithm messy GA.},
    notes = 	{EuroGP'2002, part of lutton:2002:GP},
)

@inproceedings(kuhling:2002:EuroGP,
    title = {Brute-Force Approach to Automatic Induction of Machine Code on {CISC} Architectures},
    author = {Felix K\"uhling and Krister Wolff and Peter Nordin},
    editor = {Evelyne Lutton and James A. Foster and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi},
    booktitle = {Proceedings of the 4th European Conference on Genetic Programming, EuroGP 2002},
    volume =	{2278},
    series = 	{LNCS},
    pages = {289--298},
    publisher = {Springer-Verlag},
    address = {Kinsale, Ireland},
    publisher_address = 	{Berlin},
    month = 	{3-5 April},
    year = 2002,
    keywords = 	{genetic algorithms, genetic programming},
    abstract = 	{The usual approach to address the brittleness of machine code in evolution is
to constrain mutation and crossover to ensure syntactic closure. In the novel
approach presented here we use no constraints on the operators, they all work
blindly on the binaries in memory, but we instead encapsulate the code and
trap all resulting exceptions. The new approach presented here for machine code
evolution on CISC architectures is based on the observation that modern CPUs
can cope with incorrect programmes and report errors to the operating system.
This way it is possible to return to very simple genetic operators with the
objective of increased performance. Furthermore the instruction set used by
evolved programmes is no longer limited by the genetic programming system but
only by the CPU it runs on. The mapping between evolution platform and
execution plattform becomes almost complete, ensuring correct low-level
behaviour of all CPU functions.
},
    notes = 	{EuroGP'2002, part of lutton:2002:GP},
)

@inproceedings(johnson:2002:EuroGP,
    title = {Deriving genetic programming fitness properties by static analysis},
    author = {Colin Johnson},
    editor = {Evelyne Lutton and James A. Foster and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi},
    booktitle = {Proceedings of the 4th European Conference on Genetic Programming, EuroGP 2002},
    volume =	{2278},
    series = 	{LNCS},
    pages = {299--308},
    publisher = {Springer-Verlag},
    address = {Kinsale, Ireland},
    publisher_address = 	{Berlin},
    month = 	{3-5 April},
    year = 2002,
    keywords = 	{genetic algorithms, genetic programming},
    abstract = 	{The aim of this paper is to introduce the idea of using static
analysis of computer programs as a way of measuring fitness in
genetic programming. Such techniques extract information about the
programs without explicitly running them, and in particular they infer
properties which hold across the whole of the input space of a
program. This can be applied to measure fitness, and has a number of
advantages over measuring fitness by running members of the population
on test cases. The most important advantage is that if a solution is
found then it is possible to formally trust that solution to be
correct across all inputs. This paper introduces these ideas,
discusses various ways in which they could be applied, discusses the
type of problems for which they are appropriate, and ends by giving a
simple test example and some questions for future research.
},
    notes = 	{EuroGP'2002, part of lutton:2002:GP},
)

@inproceedings(golubski:2002:EuroGP,
    title = {New Results on Fuzzy Regression by Using Genetic Programming},
    author = {Wolfgang Golubski},
    editor = {Evelyne Lutton and James A. Foster and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi},
    booktitle = {Proceedings of the 4th European Conference on Genetic Programming, EuroGP 2002},
    volume =	{2278},
    series = 	{LNCS},
    pages = {309--316},
    publisher = {Springer-Verlag},
    address = {Kinsale, Ireland},
    publisher_address = 	{Berlin},
    month = 	{3-5 April},
    year = 2002,
    keywords = 	{genetic algorithms, genetic programming},
    abstract = 	{In this paper we continue the work on symbolic fuzzy regression
problems. That means that we are interesting in finding a fuzzy
function <i>f</i> with best matches given data pairs 
<i>(x<sub>i</sub>,y<sub>i</sub>)</i> <i>1<= i <= k</i>
of fuzzy numbers. We use a genetic programming
approach for finding a suitable fuzzy function and will present
test results about linear, quadratic and cubic fuzzy functions.
},
    notes = 	{EuroGP'2002, part of lutton:2002:GP},
)

@inproceedings(ebner:2002:EuroGP,
    title = {Coevolution Produces an Arms Race Among Virtual Plants},
    author = {Marc Ebner and Adrian Grigore and Alexander Heffner and J\"urgen Albert},
    editor = {Evelyne Lutton and James A. Foster and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi},
    booktitle = {Proceedings of the 4th European Conference on Genetic Programming, EuroGP 2002},
    volume =	{2278},
    series = 	{LNCS},
    pages = {317--326},
    publisher = {Springer-Verlag},
    address = {Kinsale, Ireland},
    publisher_address = 	{Berlin},
    month = 	{3-5 April},
    year = 2002,
    keywords = 	{genetic algorithms, genetic programming},
    abstract = 	{Creating interesting virtual worlds is a difficult task. We are using a variant
of genetic programming to automatically create plants for a virtual
environment. The plants are represented as context-free Lindenmayer systems.
OpenGL is used to visualize and evaluate the plants. Our plants have to collect
virtual sunlight through their leaves in order to reproduce successfully. Thus
we have realized an interaction between the plant and its environment. Plants
are either evaluated separately or all individuals of a population at the same
time. The experiments show that during coevolution plants grow much higher
compared to rather bushy plants when plants are evaluated in isolation.
},
    notes = 	{EuroGP'2002, part of lutton:2002:GP},
)

@inproceedings(fernandez:2002:EuroGP,
    title = {Comparing Synchronous and Asynchronous Parallel and Distributed {GP} Models},
    author = {Francisco Fernandez and G. Galeano and J. A. Gomez},
    editor = {Evelyne Lutton and James A. Foster and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi},
    booktitle = {Proceedings of the 4th European Conference on Genetic Programming, EuroGP 2002},
    volume =	{2278},
    series = 	{LNCS},
    pages = {327--336},
    publisher = {Springer-Verlag},
    address = {Kinsale, Ireland},
    publisher_address = 	{Berlin},
    month = 	{3-5 April},
    year = 2002,
    keywords = 	{genetic algorithms, genetic programming},
    abstract = 	{In this paper we present a study that analyses the respective advantages and
disadvantages of the synchronous and asynchronous versions of  island-based
genetic programming.  We also look at different measuring systems for comparing
parallel genetic programming with panmitic model.  At the same  time we show
an interesting relationship between the bloat phenomenon and the number of
individuals we use.  Finally, we study a relationship between the number of
subpopulations in parallel GP and the advantages of the asynchronous model.
},
    notes = 	{EuroGP'2002, part of lutton:2002:GP},
)