Abstract Many recent advances have been made in understanding the functional implications of the global topological properties of biological
networks through the application of complex network theory, particularly in the area of small-world…
Abstract Many recent advances have been made in understanding the functional implications of the global topological properties of biological
networks through the application of complex network theory, particularly in the area of small-world and scale-free topologies.
Computational studies which attempt to understand the structure–function relationship usually proceed by defining a representation
of cells and an affinity measure to describe their interactions. We show that this necessarily restricts the topology of the
networks that can arise—furthermore, we show that although simple topologies can be produced via representation and affinity
measures common in the literature, it is unclear how to select measures which result in complex topologies, for example, exhibiting
scale-free functionality. In this paper, we introduce the concept of the
potential network as a method in which abstract network topologies can be directly studied, bypassing any definition of shape-space and affinity
function. We illustrate the benefit of the approach by studying the evolution of idiotypic networks on a selection of scale-free
and regular topologies, finding that a key immunological property—tolerance—is promoted by bi-partite and heterogeneous topologies.
The approach, however, is applicable to the study of any network and thus has implications for both immunology and artificial
immune systems.
- Content Type Journal Article
- DOI 10.1007/s11047-009-9138-8
- Authors
- Emma Hart, Edinburgh Napier University Edinburgh Scotland, UK
- Hugues Bersini, IRIDIA, Universite de Bruxelles Bruxelles Belgium
- Francisco Santos, IRIDIA, Universite de Bruxelles Bruxelles Belgium
A new technical report was posted in the Illigal technical reports page (here). For a full list of Illigal reports click here.
Related Posts
A new technical report was posted in the Illigal technical reports page (here). For a full list of Illigal reports click here.
We have just completed the “renewal” process for the Genetic Programming and Evolvable Machines editorial board, the most exciting aspect of which is that we now have a new Associate Editor, Pauline C. Haddow (of The Norwegian University of Science and Technology, Norway) and four new regular members of the editorial board: Marc Ebner (of Eberhard Karls Universität Tübingen, Germany), Jason H. Moore (of Dartmouth Medical School, USA), Sara Silva (of Universidade de Coimbra, Portugal), and Tina Yu (of Memorial University of Newfoundland, Canada). Thanks to all of the continuing associate editors for helping with this process, and welcome to the new editors! I think that the journal will be even stronger with these additions.
The full editorial board can be found
here.
We have just completed the “renewal” process for the Genetic Programming and Evolvable Machines editorial board, the most exciting aspect of which is that we now have a new Associate Editor, Pauline C. Haddow (of The Norwegian University of Science and Technology, Norway) and four new regular members of the editorial board: Marc Ebner (of Eberhard Karls Universität Tübingen, Germany), Jason H. Moore (of Dartmouth Medical School, USA), Sara Silva (of Universidade de Coimbra, Portugal), and Tina Yu (of Memorial University of Newfoundland, Canada). Thanks to all of the continuing associate editors for helping with this process, and welcome to the new editors! I think that the journal will be even stronger with these additions.
The full editorial board can be found
here.
We received 23 submissions to the Special Issue on Parallel and Distributed Evolutionary Algorithms. This is a very healthy number, indicating strong interest in the area and good prospects for an exciting special issue. Congratulations to guest editors Marco Tomassini and Leonardo Vanneschi, thanks to all of the submitters, and thanks in advance to all of the reviewers!
We received 23 submissions to the Special Issue on Parallel and Distributed Evolutionary Algorithms. This is a very healthy number, indicating strong interest in the area and good prospects for an exciting special issue. Congratulations to guest editors Marco Tomassini and Leonardo Vanneschi, thanks to all of the submitters, and thanks in advance to all of the reviewers!
Abstract We present an approach to build sequential decision making problems which can test the generalization capabilities of classifier
systems. The approach can be applied to any sequential problem defined over a binary domain and it gen…
Abstract We present an approach to build sequential decision making problems which can test the generalization capabilities of classifier
systems. The approach can be applied to any sequential problem defined over a binary domain and it generates a new problem
with bounded sequential difficulty and bounded generalization difficulty. As an example, we applied the approach to generate
two problems with simple sequential structure, huge number of states (more than a million), and many generalizations. These
problems are used to compare a classifier system with effective generalization (XCS) and a learner without generalization
(Q-learning). The experimental results confirm what was previously found mainly using single-step problems: also in sequential
problems with huge state spaces, XCS can generalize effectively by detecting those substructures that are necessary for optimal
sequential behavior.
- Content Type Journal Article
- DOI 10.1007/s12065-009-0019-y
- Authors
- Martin V. Butz, University of Würzburg Röntgenring 11 97070 Würzburg Germany
- Pier Luca Lanzi, Politecnico di Milano Dipartimento di Elettronica e Informazione Milan Italy
A considerable amount of research in genetic and evolutionary computing is concerned to some degree with self-adaptation — that is, with the adaptation and improvement of an evolutionary system over evolutionary time. (Try searching for “self-adaptive” in the GPEM journal search and GP-bibliography search boxes on the left.) This work connects not only to research in evolutionary biology but also to research on the origins of life, since it is concerned with the ways in which adaptive systems can themselves arise and become more adaptive.
In this context it is interesting to see today’s announcement of an apparent breakthrough in origins of life research, on a possible scenario for the emergence of RNA on prebiotic Earth. This is work by Matthew W. Powner, Beatrice Gerland, and John D. Sutherland at the University of Manchester. There’s a write-up in the New York Times, and the full report and a commentary by Jack W. Szostak are available in today’s Nature (subscription required for full text).
Among the reasons this might interest GPEM readers is the fact that the discovery was made through an intensive search of the space of chemical reaction sequences. This may be a search space within which genetic and evolutionary computation can help to find new and interesting things, if the right kinds of computational chemistry simulation systems (of which there are many) can be used for fitness testing on with the right kinds of problems. Putting all of this together to make significant discoveries will be non-trivial, but it seems to me to have potential.
Incidentally, searching for “origins” or “chemistry” in the journal, using the top search box on the left, produces several items of related interest that were published previously in GPEM.
A considerable amount of research in genetic and evolutionary computing is concerned to some degree with self-adaptation — that is, with the adaptation and improvement of an evolutionary system over evolutionary time. (Try searching for “self-adaptive” in the GPEM journal search and GP-bibliography search boxes on the left.) This work connects not only to research in evolutionary biology but also to research on the origins of life, since it is concerned with the ways in which adaptive systems can themselves arise and become more adaptive.
In this context it is interesting to see today’s announcement of an apparent breakthrough in origins of life research, on a possible scenario for the emergence of RNA on prebiotic Earth. This is work by Matthew W. Powner, Beatrice Gerland, and John D. Sutherland at the University of Manchester. There’s a write-up in the New York Times, and the full report and a commentary by Jack W. Szostak are available in today’s Nature (subscription required for full text).
Among the reasons this might interest GPEM readers is the fact that the discovery was made through an intensive search of the space of chemical reaction sequences. This may be a search space within which genetic and evolutionary computation can help to find new and interesting things, if the right kinds of computational chemistry simulation systems (of which there are many) can be used for fitness testing on with the right kinds of problems. Putting all of this together to make significant discoveries will be non-trivial, but it seems to me to have potential.
Incidentally, searching for “origins” or “chemistry” in the journal, using the top search box on the left, produces several items of related interest that were published previously in GPEM.
Genetic Programming and Evolvable Machines
Tenth Anniversary Special Issue on Progress in Genetic Programming and Evolvable Machines
(Revised May 19, 2009; please note revised title and deadlines. 2nd revision July 15, 2009. 3rd revision September 25, 2009; please note revised schedule)
Genetic Programming and Evolvable Machines is ten years old in 2010. To mark this, a prestigious special issue of the journal will be published. A number of articles by leading figures have already been commissioned:
- “Theoretical Results in Genetic Programming: The next ten years?” by Riccardo Poli, William B. Langdon, Nic McPhee and Leonardo Vanneschi
- “Human Competitive Results Using Genetic Programming” by John Koza
- “Genetic Programming and Evolvable Machines: Ten Years of Reviews” by William B. Langdon and Steven Gustafson
Open submissions
We encourage the submission of high quality papers that review or analyze progress in the field, present the state-of-the-art in the evolution of software and hardware, describe promising new approaches or application areas, or foundational topics in genetic programming and evolvable machines.
Subjects include, but are not limited to:
– Theoretical understanding of Genetic Programming
– Important Application Areas of Genetic Programming and Evolvable Machines
– New approaches and paradigms
– Fundamental Issues
– Wide ranging reviews and/or analysis of Research in Genetic and Evolvable Machines
Important Dates
– Paper submission deadline: November 23, 2009
– Notification of acceptance: January 15, 2009
– Final manuscript: February 15, 2010
Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by, other journals.All open submissions will be peer reviewed subject to the standards of the journal. Manuscripts based on previously published conference papers must be extended substantially.
Springer offers authors, editors and reviewers of Genetic Programming and Evolvable Machines a web-enabled online manuscript submission and review system. Our online system offers authors the ability to track the review process of their manuscript.
Manuscripts should be submitted to: http://GENP.edmgr.com. This online system offers easy and straightforward log-in and submission procedures, and supports a wide range of submission file formats.
All enquiries on this special issue by prospective authors should be sent to the guest editors at the addresses below.
Guest editors
Julian Miller
Department of Electronics
University of York,
Heslington, York,
YO10 5DD, UK
jfm7@ohm.york.ac.uk
Riccardo Poli
School of Computer Science and Electronic Engineering,
University of Essex,
Wivenhoe Park, Colchester,
CO4 3SQ, UK
rpoli@essex.ac.uk
Editor-in-Chief: Lee Spector, Hampshire College
Founding Editor: Wolfgang Banzhaf, Memorial University of Newfoundland
Journal Website: www.springer.com/10710
Genetic Programming and Evolvable Machines
Tenth Anniversary Special Issue on Progress in Genetic Programming and Evolvable Machines
(Revised May 19, 2009; please note revised title and deadlines. 2nd revision July 15, 2009. 3rd revision September 25, 2009; please note revised schedule)
Genetic Programming and Evolvable Machines is ten years old in 2010. To mark this, a prestigious special issue of the journal will be published. A number of articles by leading figures have already been commissioned:
- “Theoretical Results in Genetic Programming: The next ten years?” by Riccardo Poli, William B. Langdon, Nic McPhee and Leonardo Vanneschi
- “Human Competitive Results Using Genetic Programming” by John Koza
- “Genetic Programming and Evolvable Machines: Ten Years of Reviews” by William B. Langdon and Steven Gustafson
Open submissions
We encourage the submission of high quality papers that review or analyze progress in the field, present the state-of-the-art in the evolution of software and hardware, describe promising new approaches or application areas, or foundational topics in genetic programming and evolvable machines.
Subjects include, but are not limited to:
– Theoretical understanding of Genetic Programming
– Important Application Areas of Genetic Programming and Evolvable Machines
– New approaches and paradigms
– Fundamental Issues
– Wide ranging reviews and/or analysis of Research in Genetic and Evolvable Machines
Important Dates
– Paper submission deadline: November 23, 2009
– Notification of acceptance: January 15, 2009
– Final manuscript: February 15, 2010
Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by, other journals.All open submissions will be peer reviewed subject to the standards of the journal. Manuscripts based on previously published conference papers must be extended substantially.
Springer offers authors, editors and reviewers of Genetic Programming and Evolvable Machines a web-enabled online manuscript submission and review system. Our online system offers authors the ability to track the review process of their manuscript.
Manuscripts should be submitted to: http://GENP.edmgr.com. This online system offers easy and straightforward log-in and submission procedures, and supports a wide range of submission file formats.
All enquiries on this special issue by prospective authors should be sent to the guest editors at the addresses below.
Guest editors
Julian Miller
Department of Electronics
University of York,
Heslington, York,
YO10 5DD, UK
jfm7@ohm.york.ac.uk
Riccardo Poli
School of Computer Science and Electronic Engineering,
University of Essex,
Wivenhoe Park, Colchester,
CO4 3SQ, UK
rpoli@essex.ac.uk
Editor-in-Chief: Lee Spector, Hampshire College
Founding Editor: Wolfgang
Banzhaf, Memorial University of Newfoundland
Journal Website: www.springer.com/10710
Abstract In recent years evolutionary and immune-inspired approaches have been applied to content-based and collaborative filtering.
These biologically inspired approaches are well suited to problems like profile adaptation in content-based…
Abstract In recent years evolutionary and immune-inspired approaches have been applied to content-based and collaborative filtering.
These biologically inspired approaches are well suited to problems like profile adaptation in content-based filtering and
rating sparsity in collaborative filtering, due to their distributed and dynamic characteristics. In this paper we introduce
the relevant concepts and algorithms and review the state of the art in evolutionary and immune-inspired information filtering.
Our intention is to promote the interplay between information filtering and biologically inspired computing and boost developments
in this emerging interdisciplinary field.
- Content Type Journal Article
- DOI 10.1007/s11047-009-9126-z
- Authors
- Nikolaos Nanas, The Open University Computing Department Milton Keynes UK
- Anne de Roeck, The Open University Computing Department Milton Keynes UK
Robert Plotkin has just published a new book for general readers on computer-automated invention and its legal and business implications. I haven’t yet read it all the way through but I see that it focuses quite heavily on invention by means of genetic and evolutionary computation. The author consulted with many researchers in developing the ideas — including myself and several other GPEM editors and authors, listed in the acknowledgments — so I think that he is well informed about the underlying science and engineering.
Robert Plotkin has just published a new book for general readers on computer-automated invention and its legal and business implications. I haven’t yet read it all the way through but I see that it focuses quite heavily on invention by means of genetic and evolutionary computation. The author consulted with many researchers in developing the ideas — including myself and several other GPEM editors and authors, listed in the acknowledgments — so I think that he is well informed about the underlying science and engineering.
