Journal Publication versus Conference Contribution?

In a recent issue of the Communications of the ACM, Moshe Vardi discusses the pros and cons of journal archival publications versus conference contributions. The upshot of his statement, which points to two recent contributions to the viewpoint columns…

In a recent issue of the Communications of the ACM, Moshe Vardi discusses the pros and cons of journal archival publications versus conference contributions. The upshot of his statement, which points to two recent contributions to the viewpoint columns of the journal [1], [2] is that perhaps it is time for Computer Scientists to shift emphasis away from conference and workshop contributions, and start publishing in journal as all other sciences do. A lively discussion followed, see among others, the opinion piece of Lance Fortnow.

As an editor myself of Genetic Programming and Evolvable Machines I have always wondered why it would be more attractive for people in our discipline to publish in conference venues than in archival journals. Are there not enough journals to allow for scientific progress? Or is there a dire need to communicate with colleagues in spatial co-location? Well, to my mind, none of the two! We are not the types of people that wanted to discuss our results to extreme length. Our conferences and workshops usually operate under tight time constraints, and one to three questions is about the average a presenter receives, anything else would eat into the next presenter’s time and is discouraged. Also, the number of journals now accepting work from our field has grown over the years to a very reasonable number so that there is no shortage of places where quality work could find a home.

What is it then, that makes us submit and publish so much at conferences? Possible explanations are the existence of deadlines and the incremental nature of much of the work published. The existence of deadlines is a valuable selection pressure in our hectic times where everything is under the dictate of time-driven priorities. It can only be mimicked by journals through the introduction of regular “special issues” which also come with this requirement, and usually are successful in attracting work. As for the second possible explanation, I’d like to cite from [1] on the pitfalls of program committee work: “And arguably it is the more innovative papers that suffer because they are time consuming to read and understand, so they are the most likely to be either completely misunderstood or underappreciated by an increasingly error-prone process.” So while innovative work has a harder time at conferences, “our culture creates more units to review with a lower density of new ideas.” It is not only that we get to review smaller pieces of work, we are also more busy, with all the workshops and conferences that make us look at these papers. “Genuinely innovative papers that have issues, but could have been conditionally accepted, are all too often rejected in this climate of negativism. So the less ambitious, but well-executed work trumps what could have been the more exciting result.” Those would have to be revised and revised and revised again, and there is no time to do this for conferences. Journal articles, on the other hand, can be worked on for a long time, if need be, and there is no time pressure except for the fact that delays could be unbearable and make results obsolete.

In the end, however, it is the impact of the work that counts most. And it is my experience that a carefully edited journal paper is worth the effort, as it produces impact on a scale that conference papers have diffulty to achieve.

[1] K. Birman and F.B. Schneider. Comm. ACM, 52(5) 2009, p. 34
[2] J. Crowcroft, S. Keshav, and N. McKeown, Comm. ACM, 52(1) 2009, p. 27

GPEM 10(3) now available online

The third issue of volume 10 of Genetic Programming and Evolvable Machines is now available online, containing the following articles:A three-step decomposition method for the evolutionary design of sequential logic circuitsby Houjun Liang, Wenjian Luo…

The third issue of volume 10 of Genetic Programming and Evolvable Machines is now available online, containing the following articles:

A three-step decomposition method for the evolutionary design of sequential logic circuits
by Houjun Liang, Wenjian Luo and Xufa Wang
Evolutionary design of evolutionary algorithms
by Laura Dioşan and Mihai Oltean
Semantic analysis of program initialisation in genetic programming
by Lawrence Beadle and Colin G. Johnson

Additional awards at GECCO-2009

There were several awards presented at the GECCO-2009 conference aside from the Human-Competitive Results Awards (Humies) awards about which Wolfgang posted previously, and for which I’ve listed the other winners below. Of particular interest to reader…

There were several awards presented at the GECCO-2009 conference aside from the Human-Competitive Results Awards (Humies) awards about which Wolfgang posted previously, and for which I’ve listed the other winners below. Of particular interest to readers of this blog may be the Best Paper awards from each of the technical tracks; these are generally awarded for exciting new results, several of which may soon be appearing in more complete form in our field’s journals. In addition, this year was the first year of the SIGEVO GECCO Impact Award, for the papers with the most citations from the GECCO conference 10 years ago.
Congratulations to all of the winners!
2009 SIGEVO GECCO Impact Awards, for the papers with the most citations from GECCO 1999
M. Pelikan, D. Goldberg, E. Cantu-Paz: “BOA: The Bayesian Optimization Algorithm”
Citations: 447
S. Hofmeyer, S. Forrest: “Immunity by Design: An Artificial Immune System”
Citations: 212
Humies BRONZE MEDALS
Perez, Olague: “Evolutionary Learning of Local Descriptor Operators for Object Recognition”
AND Hauptpman, Elyasay, Sipper, Karman: “GP to Evolve Solvers for the Rush Hour Problem”
Humies SILVER MEDAL
Shahzad, Zahid, Farooq, Khayam: “GA+PSO for User ID on Smart Phones”
Humies GOLD MEDAL
Forrest, Le Goues, Nguyen, Weimer: “GP for Automated Software Repair”
2009 GECCO Best Paper Awards
Ant Colony Optimization and Swarm Intelligence: “Parallel Shared Memory Strategies for Ant-Based Optimization Algorithms” by T. Bui, T. Nguyen, J. R. Rizzo Jr.
Artificial Life, Evolutionary Robotics, Adaptive Behavior, Evolvable Hardware: “How Novelty Search Escapes the Deceptive Trap of Learning to Learn” by S. Risi, S. D. Vanderbleek, C. E. Hughes, K. O. Stanley
Bioinformatics and Computational Biology Modeling: “Evolutionary Fitness for DNA Motif Discovery” by S. Rahmann, T. Marschall, F. Behler, O. Kramer
Combinatorial Optimization and Metaheuristics: “Fixed-Parameter Evolutionary Algorithms and the Vertex Cover Problem” by S. Kratsch, F. Neumann
Estimation of Distribution Algorithms: “EDA-RL: Estimation of Distribution Algorithms for Reinforcement Learning Problems” by H. Handa
AND
“Approximating the Search Distribution to the Selection Distribution in EDAs” by S. I. Valdez-Peña, A. Hernández-Aguirre, S. Botello-Rionda
Evolution Strategies and Evolutionary Programming: “Efficient Natural Evolution Strategies” by Y. Sun, D. Wierstra, T. Schaul, J. Schmidhuber
Evolutionary Multiobjective Optimization: “Multiplicative Approximations and the Hypervolume Indicator” by T. Friedrich, C. Horoba, F. Neumann
Generative and Developmental Systems: “The Sensitivity of HyperNEAT to Different Geometric Representations of a Problem” by J. Clune, C. Ofria, R. T. Pennock
Genetic Algorithms: “Tunneling Between Optima: Partition Crossover for the Traveling Salesman Problem” by D. Whitley, A. Howe, D. Hains
Genetic Programming: “A Genetic Programming Approach to Automated Software Repair” by S. Forrest, T.V. Nguyen, W. Weimer, C. Le Goues
Genetics-Based Machine Learning: “Learning Sensorimotor Control Structures with XCSF” by M. V. Butz, G. K. M. Pedersen, P. O. Stalph
AND
“New Entropy Model for Extraction of Structural Information from XCS Population” by W. K. Park, J. C. Oh
Parallel Evolutionary Systems: “Strategies to Minimise the Total Run Time of Cyclic Graph Based Genetic Programming with GPUs” by T. E. Lewis, G. D. Magoulas
Real World Applications: “Optimizing Low-Discrepancy Sequences with an Evolutionary Algorithm” by F.-M. De Rainville, C. Gagné, O. Teytaud, D. Laurendeau
Search Based Software Engineering: “Software Project Planning for Robustness and Completion Time in the Presence of Uncertainty using Multi Objective Search Based Software Engineering” by S. Gueorguiev, M. Harman, G. Antoniol
Theory: “Dynamic Evolutionary Optimisation: An Analysis of Frequency and Magnitude of Change” by P. Rohlfshagen. P. K. Lehre, X. Yao
GECCO Graduate Student Workshop: “Learnable Evolution Model Performance Impaired by Binary Tournament Survival Selection” by M. Coletti (George Mason University)

GECCO Humies Award (GOLD) 2009

GP was well featured at this year’s GECCO Humies Awards. The most spectacular application which was subsequently awarded first prize (GOLD) was based on two papers by Weimer/Nguyen/Le Goues/Forrestpublished in proceedings of the 31st International Conf…

GP was well featured at this year’s GECCO Humies Awards. The most spectacular application which was subsequently awarded first prize (GOLD) was based on two papers by Weimer/Nguyen/Le Goues/Forrest
published in proceedings of the 31st International Conference on Software Engineering (ICSE) in May 2009 and Forrest/Weimer/Nguyen/Le Goes in this year’s GECCO proceedings. Both papers won awards from the respective conferences, and winning the Humies award was the “icing on the cake”.

The authors apply a specialized/improved form of Genetic Programming to locate and repair software bugs. Repairing software bugs is a time consuming and commercially very costly activity. To date, automating the process has been very difficult. The GP method proposed by our Gold Medal winners takes down the average repair time for software bugs from more than 3 hours per bug to 3 minutes.

The authors rightly claim that “showing how to use GP in the context of modern software systems and integrating GP into modern software practice will help evolutionary computation to become more widely accepted by computer scientists.”

Congratulations to the authors for a prize well deserved!

Artificial Immune Systems: structure, function, diversity and an application to biclustering

Artificial Immune Systems: structure, function, diversity and an application to biclustering
Content Type Journal ArticleDOI 10.1007/s11047-009-9145-9Authors
Leandro N. de Castro, Mackenzie University Rua da Consolação 896, Consolação Sao Paulo …

Artificial Immune Systems: structure, function, diversity and an application to biclustering

  • Content Type Journal Article
  • DOI 10.1007/s11047-009-9145-9
  • Authors
    • Leandro N. de Castro, Mackenzie University Rua da Consolação 896, Consolação Sao Paulo SP 01302-907 Brazil
    • Jon Timmis, University of York Department of Computer Science Heslington York YO10 5DD UK
    • Helder Knidel, NatComp—From Nature to Business R. do Comércio, 44, Center Santos SP 11010-140 Brazil
    • Fernando Von Zuben, University of Campinas (Unicamp) Laboratory of Bioinformatics and Bio-inspired Computing (LBiC), School of Electrical and Computer Engineering (FEEC) P.O. Box 6101 13083-970 Campinas SP Brazil

Award for David E. Goldberg

David E. Goldberg has been awarded an Evolutionary Computation Pioneer Award by the Computational Intelligence Society. More details here. Well-deserved congratulations to David, who has given so much to our field!

David E. Goldberg has been awarded an Evolutionary Computation Pioneer Award by the Computational Intelligence Society. More details here. Well-deserved congratulations to David, who has given so much to our field!

The identification and exploitation of dormancy in genetic programming

Abstract  In genetic programming, introns—fragments of code which do not contribute to the fitness of individuals—are usually viewed
negatively, and much research has been undertaken into ways of minimising their occurrence or effects. H…

Abstract  In genetic programming, introns—fragments of code which do not contribute to the fitness of individuals—are usually viewed
negatively, and much research has been undertaken into ways of minimising their occurrence or effects. However, identification
and removal of introns is often computationally expensive and sometimes intractable. We have therefore focused our attention
on one particular class of intron, which we refer to as dormant nodes. Mechanisms for locating such nodes are cheap to implement,
and reveal that the presence of dormancy can be extensive. Once identified, dormancy can be exploited in at least three ways:
improving execution efficiency, improving solution-finding performance, and simplifying program code. Experimentation shows
that the gains to be had in all three cases can be significant.

  • Content Type Journal Article
  • Category Original Paper
  • DOI 10.1007/s10710-009-9086-1
  • Authors
    • David Jackson, University of Liverpool Department of Computer Science Liverpool L69 3BX UK

SIGEVOlution Volume 3, Issue 3, is now available

I’m a little late with this post because I couldn’t reach the blog from China (where I was for the 2009 World Summit on Genetic and Evolutionary Computation). Aside from the web access issues it was an interesting conference and I had a great visit in China more generally. I’m now in Tokyo where I’ll visit GPEM associate editor Hitoshi Iba tomorrow, and the only web challenge appears to be in getting Blogger’s menus to appear in English rather than Japanese… but I’ve managed.
Anyway, in the interim I have received mail from Pier Luca Lanzi informing me that the latest issue of SIGEVOlution, the SIGEVO newsletter, has just been released. It is available from http://www.sigevolution.org and features:
  • An Interview with John H. Holland with an introduction by Lashon Booker
  • It’s Not Junk! by Clare Bates Congdon, H. Rex Gaskins, Gerardo M. Nava & Carolyn Mattingly
  • Car racing @ CIG-2008
  • GECCO-2009 competitions
  • New issues of journals
  • Calls & calendar
Don’t be confused by the “Autumn 2008” cover date. It is indeed a new issue that just came out in June, 2009, but the volume/date correspondence has slipped (and will probably be adjusted soon).
I’m a little late with this post because I couldn’t reach the blog from China (where I was for the 2009 World Summit on Genetic and Evolutionary Computation). Aside from the web access issues it was an interesting conference and I had a great visit in China more generally. I’m now in Tokyo where I’ll visit GPEM associate editor Hitoshi Iba tomorrow, and the only web challenge appears to be in getting Blogger’s menus to appear in English rather than Japanese… but I’ve managed.
Anyway, in the interim I have received mail from Pier Luca Lanzi informing me that the latest issue of SIGEVOlution, the SIGEVO newsletter, has just been released. It is available from http://www.sigevolution.org and features:
  • An Interview with John H. Holland with an introduction by Lashon Booker
  • It’s Not Junk! by Clare Bates Congdon, H. Rex Gaskins, Gerardo M. Nava & Carolyn Mattingly
  • Car racing @ CIG-2008
  • GECCO-2009 competitions
  • New issues of journals
  • Calls & calendar
Don’t be confused by the “Autumn 2008” cover date. It is indeed a new issue that just came out in June, 2009, but the volume/date correspondence has slipped (and will probably be adjusted soon).

A Petri net representation of Bayesian message flows: importance of Bayesian networks for biological applications

Abstract  This article combines Bayes’ theorem with flows of probabilities, flows of evidences (likelihoods), and fundamental concepts
for learning Bayesian networks as biological models from data. There is a huge amount of biological appl…

Abstract  

This article combines Bayes’ theorem with flows of probabilities, flows of evidences (likelihoods), and fundamental concepts
for learning Bayesian networks as biological models from data. There is a huge amount of biological applications of Bayesian
networks. For example in the fields of protein modeling, pathway modeling, gene expression analysis, DNA sequence analysis,
protein–protein interaction, or protein–DNA interaction. Usually, the Bayesian networks have to be learned (statistically
constructed) from array data. Then they are considered as an executable and analyzable model of the data source. To improve
that, this work introduces a Petri net representation for the propagation of probabilities and likelihoods in Bayesian networks.
The reason for doing so is to exploit the structural and dynamic properties of Petri nets for increasing the transparency
of propagation processes. Consequently the novel Petri nets are called “probability propagation nets”. By means of examples
it is shown that the understanding of the Bayesian propagation algorithm is improved. This is of particular importance for
an exact visualization of biological systems by Bayesian networks.

  • Content Type Journal Article
  • Pages 683-709
  • DOI 10.1007/s11047-009-9142-z
  • Authors
    • Kurt Lautenbach, Institut für Softwaretechnik, Universität Koblenz-Landau, Universitätsstr. 1, 56070 Koblenz, Germany
    • Alexander Pinl, Institut für Softwaretechnik, Universität Koblenz-Landau, Universitätsstr. 1, 56070 Koblenz, Germany

Stochastic Petri net models of Ca2+ signaling complexes and their analysis

Abstract  Mathematical models of Ca2+ release sites derived from Markov chain models of intracellular Ca2+ channels exhibit collective gating reminiscent of the experimentally observed phenomenon of stochastic Ca2+ excitability (i.e., puffs a…

Abstract  

Mathematical models of Ca2+ release sites derived from Markov chain models of intracellular Ca2+ channels exhibit collective gating reminiscent of the experimentally observed phenomenon of stochastic Ca2+ excitability (i.e., puffs and sparks). Ca2+ release site models are composed of a number of individual channel models whose dynamic behavior depends on the local Ca2+ concentration which is influenced by the state of all channels. We consider this application area to illustrate how stochastic
Petri nets and in particular stochastic activity networks can be used to model dynamical phenomena in cell biology. We highlight
how state-sharing composition operations as supported by the Möbius framework can represent both mean-field and spatial coupling
assumptions in a natural manner. We investigate how state-of-the-art techniques for the numerical and simulative analysis
of Markov chains associated with stochastic Petri nets scale when modeling Ca2+ signaling complexes of physiological size and complexity.

  • Content Type Journal Article
  • Pages 1045-1075
  • DOI 10.1007/s11047-009-9143-y
  • Authors
    • Ruth Lamprecht, Department of Computer Science, College of William and Mary, Williamsburg, VA 23187, USA
    • Gregory D. Smith, Department of Applied Sciences, College of William and Mary, Williamsburg, VA 23187, USA
    • Peter Kemper, Department of Computer Science, College of William and Mary, Williamsburg, VA 23187, USA