by Christian S. Perone
Eric Floehr (from Intellovations) kindly sent me the presentation he presented at PyOhio 2010. I think Eric has captured some nice features of Pyevolve which few people use, like DB Adapters, dot plotting, Interactive Mode, Real Time statistics, etc. He also presents an interesting use case where he uses Genetic Programming in order to forecast weather based on some historical data:
From Pier Luca Lanzi site, the issue features:
I’m proud to announce the Pyevolve 0.6rc1 ! This is the first release candidate, but it’s pretty stable for production use (as from this 0.6 version we are very closer to a stable codebase, thanks to community).
See the documentation site and the What’s New.
I would like to thank the people who directly or indirectly have contributed with this release: Boris Gorelik, Amit Saha, Jelle Feringa, Henrik Rudstrom, Matteo de Felice, SIGEVOlution Board, Mike Benoit, Ryan Campbell, Jonas A. Gustavsson, Soham Sadhu, Ernesto Costa, Ido Ben-Tsion, Frank Goodman, Vishal Belsare, Benjamin Bush; and a lot of people who gave us feedback of the experience with the use of Pyevolve on their applications.
For downloads, go to the Downloads section at Documentation site.
If you see something wrong with this release candidate, please create a ticket reporting your problem at Pyevolve Trac, so we can provide fixes to the official release.
Happy coding !
– Christian S. Perone
The new issue of SIGEVOlution (the newsletter of ACM Special Interest Group on Genetic and Evolutionary Computation) was released:
In this release you can read about:
Issues in applying computational intelligence
By Arthur Kordon
JavaXCSF
By Patrick O. Stalph, Martin V. Butz
And a lot of information about new PhD theses, new journal issues and about events to come.
From a publication of 28/10/2010, of the authors Saoshi Sasaki, Alexis J Comber, Hiroshi Suzuki and Chris Brunsdon:
Background
Ambulance response time is a crucial factor in patient survival. The number of emergency cases (EMS cases) requiring an ambulance is increasing due to changes in population demographics. This is decreasing ambulance response times to the emergency scene. This paper predicts EMS cases for 5-year intervals from 2020, to 2050 by correlating current EMS cases with demographic factors at the level of the census area and predicted population changes. It then applies a modified grouping genetic algorithm to compare current and future optimal locations and numbers of ambulances. Sets of potential locations were evaluated in terms of the (current and predicted) EMS case distances to those locations.
Results
Future EMS demands were predicted to increase by 2030 using the model (R2=0.71). The optimal locations of ambulances based on future EMS cases were compared with current locations and with optimal locations modelled on current EMS case data. Optimising the location of ambulance stations locations reduced the average response times by 57 seconds. Current and predicted future EMS demand at modelled locations were calculated and compared.
Conclusions
The reallocation of ambulances to optimal locations improved response times and could contribute to higher survival rates from life-threatening medical events. Modelling EMS case ‘demand’ over census areas allows the data to be correlated to population characteristics and optimal ‘supply’ locations to be identified. Comparing current and future optimal scenarios allows more nuanced planning decisions to be made. This is a generic methodology that could be used to provide evidence in support of public health planning and decision making.
Read the original paper here.
The new issue of SIGEVOlution is public available !
In this issue, you’ll found an interview with Hans-Paul Schwefel by my fellow countryman, Marcelo de Brito (or Genetic Argonaut). Congratulations to the board !
I’m proud to announce that Pyevolve is featuring on the last issue of SIGEVOlution (Volume 4, Issue 1), a newsletter from the ACM Special Interest Group on Evolutionary Computation. I would like to thank the newsletter editor Pier Luca Lanzi and the board for the corrections in the article and for the well done reformatted version of the paper.
Pyevolve is currently in version 0.5, in a few months I’ll be releasing the new 0.6 release with the new major features that are currently implemented in the development version only (you can check it at the subversion repository in sourceforge.net).
I hope you enjoy the article !
Yours,
– Christian S. Perone
UPDATE 05/10: there is an article in the Physorg too.
Sometimes we face new applications for EC, but for this I was not expecting, from Eurekalert:
WASHINGTON, Oct. 5 — Criminals are having a harder time hiding their faces, thanks to new software that helps witnesses recreate and recognize suspects using principles borrowed from the fields of optics and genetics.
(…)
His software generates its own faces that progressively evolve to match the witness’ memories. The witness starts with a general description such as “I remember a young white male with dark hair.” Nine different computer-generated faces that roughly fit the description are generated, and the witness identifies the best and worst matches. The software uses the best fit as a template to automatically generate nine new faces with slightly tweaked features, based on what it learned from the rejected faces.
“Over a number of generations, the computer can learn what face you’re looking for,” says Solomon.
Read the full article here.