# Accepted papers
The EuroGP2003 proceedings will be published by Spinger as part of their Lecture Notes in Computer Science series.
## Oral presentations
**A Simple but Theoretically-motivated Method to Control Bloat in Genetic Programming**
*
Poli R
*
This paper presents a simple method to control bloat which is based on the idea
of strategically and dynamically creating fitness {``holes{'' in
the fitness landscape which repel the population. In particular we
create holes by zeroing the fitness of a certain proportion of the
offspring that have above average length. Unlike other methods
where all individuals are penalised when length constraints are
violated, here we randomly penalise only a fixed proportion of all
the constraint-violating offspring. The paper describes the
theoretical foundation for this method and reports the results of
its empirical validation with two relatively hard test problems,
which has confirmed the effectiveness
of the approach.
**
EuroGP Session 7: Meta-search, compiler optimisation and bloat: April 15, 1120-1250
**
**An innovative application of a constrained-syntax genetic programming system to the problem ofpredicting survival of patients**
*
Bojarczuk C,
Lopes H,
Freitas A
*
This paper proposes a constrained-syntax genetic programming (GP)
algorithm for discovering classification rules in medical data sets.
The proposed GP contains several syntactic constraints to be enforced by the
system using a disjunctive normal form representation, so that individuals
represent valid rule sets that are easy to interpret. The GP is compared
with C4.5 in a real-world medical data set. This data set represents a difficult
classification problem, and a new preprocessing method was devised for mining the data.
**
EuroGP Session 3: Medical applications: April 14, 1400-1530
**
**Analysis of a Digit Concatenation Approach to Constant Creation**
*
O'Neill M,
Dempsey I,
Brabazon A,
Ryan C
*
This study examines the utility of employing
digit concatenation, as distinct from the traditional expression
based approach, for the purpose of evolving constants in
Grammatical Evolution. Digit concatenation involves creating
constants (either whole or real numbers) by concatenating digits
to form a single value. The two methods are compared using three
different problems, which are finding a static real constant,
finding dynamic real constants, and a quadratic map, which on
iteration generates a chaotic time-series.
The results indicate that the digit concatenation approach results
in a significant improvement in the best fitness obtained across all problems analysed here.
**
EuroGP Session 2: Linear GP, crossover and constants: April 14, 1130-1300
**
**Decreasing the Number of Evaluations in Evolutionary Algorithms by using a Meta-Model of the Fitness Function**
*
Ziegler J,
Banzhaf W
*
In this paper a method is presented that decreases the necessary
number of evaluations in Evolutionary Algorithms. A classifier
with confidence information is evolved to replace time consuming
evaluations during tournament selection. Experimental analysis of
a mathematical example and the application of the method to the
problem of evolving walking patterns
for quadruped robots show the potential of the presented approach.
**
EuroGP Session 4: Meta-fitness and alternative computational structures: April 14, 1600-1730
**
**Divide and Conquer: Genetic Programming Based on Multiple Branches Encoding**
*
Rodriguez K,
Oliver-Morales C
*
This paper describes an alternative genetic programming encoding,
which is based on a rooted-node with fixed-content. This rooted node combines
partial results of a set of multiple branches. Hence, this approach is named
Multiple Branches Genetic Programming. It is tested on a symbolic regression
problem and used on a Boolean domain to solve the even-n parity problem.
**
EuroGP Session 9: Multi-program integration: April 15, 1530-1630
**
**Ensemble techniques for Parallel Genetic Programming based Classifiers**
*
Folino G,
Pizzuti C,
Spezzano G
*
An extension of Cellular Genetic Programming for data
classification to induce an ensemble of predictors is presented.
Each classifier is trained on a different subset of the overall
data, then they are combined to classify new tuples by applying a
simple majority voting algorithm, like bagging. Preliminary
results on a large data set show that the ensemble of classifiers
trained on a sample of the data obtains higher accuracy than a
single classifier that uses the entire data set at a much lower
computational cost.
**
EuroGP Session 9: Multi-program integration: April 15, 1530-1630
**
**Evolutionary Design of Objects Using Scene Graphs**
*
Ebner M
*
One of the main issues in evolutionary design is how to create
three-dimensional shape. The representation needs to be general
enough such that all possible shapes can be created, yet it has to
be evolvable. That is, parent and offspring must be related. Small
changes to the genotype should lead to small changes of the
fitness of an individual. We have explored the use of scene graphs
to evolve three-dimensional shapes. Two different scene graph
representations are analyzed, the scene graph representation used
by OpenInventor and the scene graph representation used by VRML.
Both representations use internal floating point variables to
specify three-dimensional vectors, rotation axes and rotation
angles. The internal parameters are initially chosen at random,
then remain fixed during the run. We also experimented with an
evolution strategy to adapt the internal variables. Experimental
results are presented for the evolution of a wind turbine.
The VRML representation produced better results.
**
EuroGP Session 11: Alternative representations and 3-D shape design: April 16, 1000-1100
**
**Evolving Cellular Automata to Grow Microstructures**
*
Basanta D,
Bentley P,
Miodownik M,
Holm E
*
The properties of engineering structures such as cars,
cell phones or bridges rely on materials and on the properties of these
materials. The study of these properties, which are determined by the internal
architecture of the material or microstructure, has significant importance
for material scientists. One of the things needed for this study is a tool
that can create microstructural patterns. In this paper we explore the use
of a genetic algorithm to evolve the rules of an effector automata to
recreate these microstructural patterns.
**
EuroGP Session 4: Meta-fitness and alternative computational structures: April 14, 1600-1730
**
**Evolving Finite State Transducers: Some Initial Explorations**
*
Lucas S
*
Finite state transducers (FSTs) are finite state machines that map
strings in a source domain into strings in a target domain. While
there are many reports in the literature of evolving general
finite state machines, there has been much less work on evolving
FSTs.In particular, the fitness functions required for evolving
FSTs are generally different to those used for FSMs.This paper
considers three string-distance based fitness functions.We
compute their fitness distance correlations, and present results
on using two of these (Strict and Hamming) to evolve FSTs. We can
control the difficulty of the problem by the presence of short
strings in the training set, which make the learning problem
easier.In the case of the harder problem, the Hamming measure
performs best, while the Strict measure performs best on the
easier problem.
**
EuroGP Session 4: Meta-fitness and alternative computational structures: April 14, 1600-1730
**
**Evolving Hierarchical and RecursiveTeleo-Reactive Programs through Genetic Programming**
*
Kochenderfer M
*
Teleo-reactive programs and the triple tower architecture have
been proposed as a framework for linking perception and action in
agents. The triple tower architecture continually updates the
agent's knowledge of the world and evokes actions according to
teleo-reactive control structures. This paper uses block stacking
problems to demonstrate how genetic programming may be used to
evolve hierarchical and recursive teleo-reactive programs.
**
EuroGP Session 8: Feature construction and modularity and hierarchies in GP: April 15, 1345-1515
**
**Feature Construction and Selection using Genetic Programming and a Genetic Algorithm**
*
Smith M,
Bull L
*
The use of machine learning
techniques to automatically analyse data for information is becoming
increasingly widespread. In this paper we examine the use of Genetic
Programming and a Genetic Algorithm to pre-process data before it is
classified using the C4.5 decision tree learning algorithm. The Genetic
Programming is used to construct new features from those available
in the data, a potentially significant process for data mining since
it gives consideration to hidden relationships between features.
The Genetic Algorithm is used to determine which such features are the
most predictive. Using ten well-known datasets we show that our approach,
in comparison to C4.5 alone, provides marked improvement in a number of cases.
**
EuroGP Session 8: Feature construction and modularity and hierarchies in GP: April 15, 1345-1515
**
**Genetic Programming Applied to Compiler Heuristic Optimization**
*
Stephenson * |