EuroGP2003
6th European Conference on Genetic Programming
14-16 April 2003

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Conor Ryan
Terry Soule
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Edward Tsang
Riccardo Poli

EuroGP2003

Accepted papers

Springer Lecture Notes in Computer Science series 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