The deadline for submitting papers to the Genetic Programming and Evolvable Machines Special Issue on Parallel and Distributed Evolutionary Algorithms has been extended.
The new deadline is: May 15, 2009
More information about the special issue is available here.
About the GPEMjournal blog
This is the editor's blog for the journal Genetic Programming and Evolvable Machines. The official web site for the journal, maintained by the publisher (Springer) is here. The GPEMjournal blog is authored and maintained by Lee Spector.
Wednesday, April 29, 2009
Monday, April 13, 2009
A new paper by Kashtan et al. in PLoS Computational Biology presents an interesting study of the evolution of modularity, extending their previous work showing "that modular structure can spontaneously emerge if goals (environments) change over time, such that each new goal shares the same set of sub-problems with previous goals."
The evolution of modularity is a topic of longstanding interest in GP and evolutionary computation more generally, within which we often seek to evolve modular programs or structures. Many also seek to leverage the modularity of representations to accelerate evolution. A lot of the work on automatically defined functions, etc., has been concerned with these issues and I think that cross-fertilization with the new computational biology results could be fruitful.
The closest thing that I know of in the GP literature to the Kashtan et al. results is a paper by Terry Van Belle and David Ackley in GECCO 2002, in which they observed the "evolution of evolvability in experiments using genetic programming to solve a symbolic regression problem that varies in a partially unpredictable manner." Alan Robinson and I were inspired by this to do a similar experiment in PushGP, which allows modularity to arise from scratch via code self-manipulation, and we wrote it up briefly in a GECCO 2002 Workshop paper (see section 3.2).
Friday, April 10, 2009
The second issue of volume 10 of Genetic Programming and Evolvable Machines is now available online, containing the following articles:
Incorporating characteristics of human creativity into an evolutionary art algorithm
by Steve DiPaola, Liane Gabora
Using enhanced genetic programming techniques for evolving classifiers in the context of medical diagnosis
by Stephan M. Winkler, Michael Affenzeller, Stefan Wagner
Dynamic limits for bloat control in genetic programming and a review of past and current bloat theories
by Sara Silva, Ernesto Costa
A review of procedures to evolve quantum algorithms
by Adrian Gepp, Phil Stocks
Book Review: Riccardo Poli, William B. Langdon, Nicholas F. McPhee: A Field Guide to Genetic Programming
by Michael O’Neill