TY  - CONF
T1  - Towards Addressing CPU-Intensive Seismological Applications in Europe
T2  - International Supercomputing Conference
Y1  - 2013
A1  - Michele Carpené
A1  - I.A. Klampanos
A1  - Siew Hoon Leong
A1  - Emanuele Casarotti
A1  - Peter Danecek
A1  - Graziella Ferini
A1  - Andre Gemünd
A1  - Amrey Krause
A1  - Lion Krischer
A1  - Federica Magnoni
A1  - Marek Simon
A1  - Alessandro Spinuso
A1  - Luca Trani
A1  - Malcolm Atkinson
A1  - Giovanni Erbacci
A1  - Anton Frank
A1  - Heiner Igel
A1  - Andreas Rietbrock
A1  - Horst Schwichtenberg
A1  - Jean-Pierre Vilotte
AB  - Advanced application environments for seismic analysis help geoscientists to execute complex simulations to predict the behaviour of a geophysical system and potential surface observations. At the same time data collected from seismic stations must be processed comparing recorded signals with predictions. The EU-funded project VERCE (http://verce.eu/) aims to enable specific seismological use-cases and, on the basis of requirements elicited from the seismology community, provide a service-oriented infrastructure to deal with such challenges. In this paper we present VERCE’s architecture, in particular relating to forward and inverse modelling of Earth models and how the, largely file-based, HPC model can be combined with data streaming operations to enhance the scalability of experiments.We posit that the integration of services and HPC resources in an open, collaborative environment is an essential medium for the advancement of sciences of critical importance, such as seismology.
JF  - International Supercomputing Conference
CY  - Leipzig, Germany
ER  - 

TY  - CHAP
T1  - Exploiting P2P and Grid Computing Technologies for Resource Sharing to support High Performance Distributed System
T2  - Handbook of Research on P2P and Grid Systems for Service-Oriented Computing: Models, Methodologies and Applications
Y1  - 2010
A1  - Liangxiu Han
ED  - Nick Antonopoulos
ED  - Georgios Exarchakos
ED  - Maozhen Li
ED  - Antonio Liottac
JF  - Handbook of Research on P2P and Grid Systems for Service-Oriented Computing: Models, Methodologies and Applications
PB  - IGI Global publishing
VL  - 1
ER  - 

TY  - CONF
T1  - Matching Spatial Regions with Combinations of Interacting Gene Expression Patterns
T2  - Communications in Computer and Information Science
Y1  - 2008
A1  - van Hemert, J. I.
A1  - Baldock, R. A.
ED  - M. Elloumi
ED  - \emph
ED  - et al
KW  - biomedical
KW  - data mining
KW  - DGEMap
KW  - e-Science
AB  - The Edinburgh Mouse Atlas aims to capture in-situ gene expression patterns in a common spatial framework. In this study, we construct a grammar to define spatial regions by combinations of these patterns. Combinations are formed by applying operators to curated gene expression patterns from the atlas, thereby resembling gene interactions in a spatial context. The space of combinations is searched using an evolutionary algorithm with the objective of finding the best match to a given target pattern. We evaluate the method by testing its robustness and the statistical significance of the results it finds.
JF  - Communications in Computer and Information Science
PB  - Springer Verlag
ER  - 

TY  - JOUR
T1  - Comparing Evolutionary Algorithms on Binary Constraint Satisfaction Problems
JF  - IEEE Transactions on Evolutionary Computation
Y1  - 2003
A1  - Craenen, B. G. W.
A1  - Eiben, A. E.
A1  - van Hemert, J. I.
KW  - constraint satisfaction
AB  - Constraint handling is not straightforward in evolutionary algorithms (EA) since the usual search operators, mutation and recombination, are `blind' to constraints. Nevertheless, the issue is highly relevant, for many challenging problems involve constraints. Over the last decade numerous EAs for solving constraint satisfaction problems (CSP) have been introduced and studied on various problems. The diversity of approaches and the variety of problems used to study the resulting algorithms prevents a fair and accurate comparison of these algorithms. This paper aligns related work by presenting a concise overview and an extensive performance comparison of all these EAs on a systematically generated test suite of random binary CSPs. The random problem instance generator is based on a theoretical model that fixes deficiencies of models and respective generators that have been formerly used in the Evolutionary Computing (EC) field.
VL  - 7
UR  - http://ieeexplore.ieee.org/xpl/abs_free.jsp?isNumber=27734&prod=JNL&arnumber=1237162&arSt=+424&ared=+444&arAuthor=+Craenen%2C+B.G.W.%3B++Eiben%2C+A.E.%3B++van+Hemert%2C+J.I.&arNumber=1237162&a_id0=1237161&a_id1=1237162&a_id2=1237163&a_id3=1237164&a_id4=12
ER  - 

TY  - JOUR
T1  - The pervasiveness of evolution in GRUMPS software
JF  - Softw., Pract. Exper.
Y1  - 2003
A1  - Evans, Huw
A1  - Atkinson, Malcolm P.
A1  - Brown, Margaret
A1  - Cargill, Julie
A1  - Crease, Murray
A1  - Draper, Steve
A1  - Gray, Philip D.
A1  - Thomas, Richard
VL  - 33
ER  - 

TY  - CONF
T1  - Adaptive Genetic Programming Applied to New and Existing Simple Regression Problems
T2  - Springer Lecture Notes on Computer Science
Y1  - 2001
A1  - Eggermont, J.
A1  - van Hemert, J. I.
ED  - J. Miller
ED  - Tomassini, M.
ED  - P. L. Lanzi
ED  - C. Ryan
ED  - A. G. B. Tettamanzi
ED  - W. B. Langdon
KW  - data mining
AB  - In this paper we continue our study on adaptive genetic pro-gramming. We use Stepwise Adaptation of Weights to boost performance of a genetic programming algorithm on simple symbolic regression problems. We measure the performance of a standard GP and two variants of SAW extensions on two different symbolic regression prob-lems from literature. Also, we propose a model for randomly generating polynomials which we then use to further test all three GP variants.
JF  - Springer Lecture Notes on Computer Science
PB  - Springer-Verlag, Berlin
SN  - 9-783540-418993
ER  - 

TY  - CONF
T1  - The GRUMPS Architecture: Run-time Evolution in a Large Scale Distributed System
T2  - Proceedings of the Workshop on Engineering Complex Object-Oriented Solutions for Evolution (ECOOSE), held as part of OOPSLA 2001.
Y1  - 2001
A1  - Evans, Huw
A1  - Peter Dickman
A1  - Malcolm Atkinson
AB  - This paper describes the first version of the distributed programming architecture for the Grumps1 project. The architecture consists of objects that communicate in terms of both asynchronous and synchronous events. A novel three-level extensible naming scheme is discussed that allows Grumps developers to deploy systems that can refer to entities not identified at the time when the Grumps system and application-level code were implemented. Examples detailing how the topology of a Grumps system may be changed at run-time and how new object implementations may be distributed during system execution are given. The separation of policy from mechanism is shown to be a major part of how system evolution is supported and this is made even more flexible when expressed through the use of Java interfaces for crucial core concepts.
JF  - Proceedings of the Workshop on Engineering Complex Object-Oriented Solutions for Evolution (ECOOSE), held as part of OOPSLA 2001.
ER  - 

TY  - BOOK
T1  - GRUMPS Summer Anthology, 2001
Y1  - 2001
A1  - Atkinson, M.
A1  - Brown, M.
A1  - Cargill, J.
A1  - Crease, M.
A1  - Draper, S.
A1  - Evans, H.
A1  - Gray, P.
A1  - Mitchell, C.
A1  - Ritchie, M.
A1  - Thomas, R.
AB  - This is the first collection of papers from GRUMPS [http://grumps.dcs.gla.ac.uk]. The project only started up in February 2001, and this collection (frozen at 1 Sept 2001) shows that it got off to a productive start. Versions of some of these papers have been submitted to conferences and workshops: the website will have more information on publication status and history.  GRUMPS decided to begin with a first study, partly to help the team coalesce. This involved installing two pieces of software in a first year computing science lab: one (the "UAR") to record a large volume of student actions at a low level with a view to mining them later, another (the "LSS") directly designed to assist tutor-student interaction. Some of the papers derive from that, although more are planned. Results from this first study can be found on the website. The project also has a link to UWA in Perth, Western Australia, where related software has already been developed and used as described in one of the papers. Another project strand concerns using handsets in lecture theatres to support interactivity there, as two other papers describe. As yet unrepresented in this collection, GRUMPS will also be entering the bioinformatics application area.    The GRUMPS project operates on several levels. It is based in the field of Distributed Information Management (DIM), expecting to cover both mobile and static nodes, synchronous and detached clients, high and low volume data sources. The specific focus of the project (see the original proposal on the web site) is to address records of computational activity (where any such pre-existing usage might have extra record collection installed) and data experimentation, where the questions to be asked of the data emerge concurrently with data collection which will therefore be dynamically modifiable: a requirement that further pushes on the space of DIM. The level above concerns building and making usable tools for asking questions of the data, or rather of the activities that generate the data. Above that again is the application domain level: what the original computational activities serve, education and bioinformatics being two identified cases.  The GRUMPS team is therefore multidisciplinary, from DIM architecture researchers to educational evaluators. The mix of papers reflects this.
PB  - Academic Press
ER  - 

TY  - CONF
T1  - Stepwise Adaptation of Weights for Symbolic Regression with Genetic Programming
T2  - Proceedings of the Twelfth Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'00)
Y1  - 2000
A1  - Eggermont, J.
A1  - van Hemert, J. I.
ED  - van den Bosch, A.
ED  - H. Weigand
KW  - data mining
KW  - genetic programming
AB  - In this paper we continue study on the Stepwise Adaptation of Weights (SAW) technique. Previous studies on constraint satisfaction and data clas-sification have indicated that SAW is a promising technique to boost the performance of evolutionary algorithms. Here we use SAW to boost per-formance of a genetic programming algorithm on simple symbolic regression problems. We measure the performance of a standard GP and two variants of SAW extensions on two different symbolic regression problems.
JF  - Proceedings of the Twelfth Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'00)
PB  - BNVKI, Dutch and the Belgian AI Association
ER  - 

TY  - CONF
T1  - Adapting the Fitness Function in GP for Data Mining
T2  - Springer Lecture Notes on Computer Science
Y1  - 1999
A1  - Eggermont, J.
A1  - Eiben, A. E.
A1  - van Hemert, J. I.
ED  - R. Poli
ED  - P. Nordin
ED  - W. B. Langdon
ED  - T. C. Fogarty
KW  - data mining
KW  - genetic programming
AB  - In this paper we describe how the Stepwise Adaptation of Weights (SAW) technique can be applied in genetic programming. The SAW-ing mechanism has been originally developed for and successfully used in EAs for constraint satisfaction problems. Here we identify the very basic underlying ideas behind SAW-ing and point out how it can be used for different types of problems. In particular, SAW-ing is well suited for data mining tasks where the fitness of a candidate solution is composed by `local scores' on data records. We evaluate the power of the SAW-ing mechanism on a number of benchmark classification data sets. The results indicate that extending the GP with the SAW-ing feature increases its performance when different types of misclassifications are not weighted differently, but leads to worse results when they are.
JF  - Springer Lecture Notes on Computer Science
PB  - Springer-Verlag, Berlin
SN  - 3-540-65899-8
ER  - 

TY  - CONF
T1  - Comparing genetic programming variants for data classification
T2  - Proceedings of the Eleventh Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'99)
Y1  - 1999
A1  - Eggermont, J.
A1  - Eiben, A. E.
A1  - van Hemert, J. I.
ED  - E. Postma
ED  - M. Gyssens
KW  - classification
KW  - data mining
KW  - genetic programming
AB  - This article is a combined summary of two papers written by the authors.  Binary data classification problems (with exactly two disjoint classes) form an important application area of machine learning techniques, in particular genetic programming (GP). In this study we compare a number of different variants of GP applied to such problems whereby we investigate the effect of two significant changes in a fixed GP setup in combination with two different evolutionary models
JF  - Proceedings of the Eleventh Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'99)
PB  - BNVKI, Dutch and the Belgian AI Association
ER  - 

TY  - CONF
T1  - A comparison of genetic programming variants for data classification
T2  - Springer Lecture Notes on Computer Science
Y1  - 1999
A1  - Eggermont, J.
A1  - Eiben, A. E.
A1  - van Hemert, J. I.
ED  - D. J. Hand
ED  - J. N. Kok
ED  - M. R. Berthold
KW  - classification
KW  - data mining
KW  - genetic programming
AB  - In this paper we report the results of a comparative study on different variations of genetic programming applied on binary data classification problems. The first genetic programming variant is weighting data records for calculating the classification error and modifying the weights during the run. Hereby the algorithm is defining its own fitness function in an on-line fashion giving higher weights to `hard' records. Another novel feature we study is the atomic representation, where `Booleanization' of data is not performed at the root, but at the leafs of the trees and only Boolean functions are used in the trees' body.  As a third aspect we look at generational and steady-state models in combination of both features.
JF  - Springer Lecture Notes on Computer Science
PB  - Springer-Verlag, Berlin
SN  - 3-540-66332-0
ER  - 

TY  - CONF
T1  - Mondriaan Art by Evolution
T2  - Proceedings of the Eleventh Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'99)
Y1  - 1999
A1  - van Hemert, J. I.
A1  - Eiben, A. E.
ED  - E. Postma
ED  - M. Gyssens
KW  - evolutionary art
AB  - Here we show an application that generates images resembling art as it was produced by Mondriaan, a Dutch artist, well known for his minimalistic and pure abstract pieces of art. The current version generates images using a linear chromosome and a recursive function as a decoder.
JF  - Proceedings of the Eleventh Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'99)
PB  - BNVKI, Dutch and the Belgian AI Association
ER  - 

TY  - CONF
T1  - Population dynamics and emerging features in AEGIS
T2  - Proceedings of the Genetic and Evolutionary Computation Conference
Y1  - 1999
A1  - Eiben, A. E.
A1  - Elia, D.
A1  - van Hemert, J. I.
ED  - W. Banzhaf
ED  - J. Daida
ED  - Eiben, A. E.
ED  - M. H. Garzon
ED  - V. Honavar
ED  - M. Jakiela
ED  - R. E. Smith
KW  - dynamic problems
AB  - We describe an empirical investigation within an artificial world, aegis, where a population of animals and plants is evolving. We compare different system setups in search of an `ideal' world that allows a constantly high number of inhabitants for a long period of time. We observe that high responsiveness at individual level (speed of movement) or population level (high fertility) are `ideal'.  Furthermore, we investigate the emergence of the so-called mental features of animals determining their social, consumptional and aggressive behaviour. The tests show that being socially oriented is generally advantageous, while agressive behaviour only emerges under specific circumstances.
JF  - Proceedings of the Genetic and Evolutionary Computation Conference
PB  - Morgan Kaufmann Publishers, San Francisco
ER  - 

TY  - CHAP
T1  - SAW-ing EAs: adapting the fitness function for solving constrained problems
T2  - New ideas in optimization
Y1  - 1999
A1  - Eiben, A. E.
A1  - van Hemert, J. I.
ED  - D. Corne
ED  - M. Dorigo
ED  - F. Glover
KW  - constraint satisfaction
AB  - In this chapter we describe a problem independent method for treating constrain ts in an evolutionary algorithm. Technically, this method amounts to changing the defini tion of the fitness function during a run of an EA, based on feedback from the search pr ocess. Obviously, redefining the fitness function means redefining the problem to be sol ved. On the short term this deceives the algorithm making the fitness values deteriorate , but as experiments clearly indicate, on the long run it is beneficial. We illustrate t he power of the method on different constraint satisfaction problems and point out other application areas of this technique.
JF  - New ideas in optimization
PB  - McGraw-Hill, London
ER  - 

TY  - CONF
T1  - Extended abstract: Solving Binary Constraint Satisfaction Problems using Evolutionary Algorithms with an Adaptive Fitness Function
T2  - Proceedings of the Xth Netherlands/Belgium Conference on Artificial Intelligence (NAIC'98)
Y1  - 1998
A1  - Eiben, A. E.
A1  - van Hemert, J. I.
A1  - Marchiori, E.
A1  - Steenbeek, A. G.
ED  - la Poutré, J. A.
ED  - van den Herik, J.
KW  - constraint satisfaction
JF  - Proceedings of the Xth Netherlands/Belgium Conference on Artificial Intelligence (NAIC'98)
PB  - BNVKI, Dutch and the Belgian AI Association
N1  - Abstract of \cite{EHMS98}
ER  - 

TY  - JOUR
T1  - Graph Coloring with Adaptive Evolutionary Algorithms
JF  - Journal of Heuristics
Y1  - 1998
A1  - Eiben, A. E.
A1  - van der Hauw, J. K.
A1  - van Hemert, J. I.
KW  - constraint satisfaction
KW  - graph colouring
AB  - This paper presents the results of an experimental investigation on solving graph coloring problems with Evolutionary Algorithms (EA). After testing different algorithm variants we conclude that the best option is an asexual EA using order-based representation and an adaptation mechanism that periodically changes the fitness function during the evolution.  This adaptive EA is general, using no domain specific knowledge, except, of course, from the decoder (fitness function). We compare this adaptive EA to a powerful traditional graph coloring technique DSatur and the Grouping GA on a wide range of problem instances with different size, topology and edge density. The results show that the adaptive EA is superior to the Grouping GA and outperforms DSatur on the hardest problem instances. Furthermore, it scales up better with the problem size than the other two algorithms and indicates a linear computational complexity.
PB  - Kluwer Academic Publishers
VL  - 4
ER  - 

TY  - CONF
T1  - Solving Binary Constraint Satisfaction Problems using Evolutionary Algorithms with an Adaptive Fitness Function
T2  - Springer Lecture Notes on Computer Science
Y1  - 1998
A1  - Eiben, A. E.
A1  - van Hemert, J. I.
A1  - Marchiori, E.
A1  - Steenbeek, A. G.
ED  - Eiben, A. E.
ED  - Th. B{\"a}ck
ED  - M. Schoenauer
ED  - H.-P. Schwefel
KW  - constraint satisfaction
AB  - This paper presents a comparative study of Evolutionary Algorithms (EAs) for Constraint Satisfaction Problems (CSPs). We focus on EAs where fitness is based on penalization of constraint violations and the penalties are adapted during the execution. Three different EAs based on this approach are implemented. For highly connected constraint networks, the results provide further empirical support to the theoretical prediction of the phase transition in binary CSPs.
JF  - Springer Lecture Notes on Computer Science
PB  - Springer-Verlag, Berlin
ER  -