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Press Release of Genalytics Model 6.0

ThomasNet reports that Genalytics released Genalytics Model 6.0, that uses genetic algorithms to identify credit risks, anticipate customer attrition, and detect probable bankruptcies:


Genalytics Model 6.0 transforms large amounts of transaction data into predictive intelligence and uses genetic algorithms to automatically search and identify key combinations of time series variables found in any dataset. By studying time series patterns, organizations can identify credit risks, anticipate customer attrition, and detect probable bankruptcies. Software also allows users to format modeling output for Java and SPSS.


Doug Newell, founder of Genalytics, gave a talk at GECCO 2005, as reported here.



John Holland in Canada

Mahantesh reports that John H. Holland is visiting St. John's, Newfoundland in Canada (here). Here is a synopsis of his talk on complex adaptive systems:


The Department of Computer Science hosts the world-famous computer scientist and inventor of Genetic Algorithms (GA's), Dr. John Holland, from the University of Michigan. His talk on Complex Adaptive Systems (CAS) will discuss the study of complexity, innovation, and relations between CAS and GA's. Sponsor: Computer Science.


No doubt this was the doing of Wolfgang Banzhaf, head of CS.



Embracing mistakes

Sometimes it is important to make mistakes, lots of them, on the way to doing something new (here).



Genetic algorithm for Ruby

Rubyforge has a fairly recent posting for a package called rGenetic:


rGenetic is a genetic algorithm package for Ruby. The idea is to develop an easily customizable suite, allowing simple integration into a problem requiring genetic algorithms or evolutionary programming.

Matthew Linnell is the project administrator. See here for additional detail.



Fast GA on Freshmeat

Freshmeat has a new posting for a publicly available genetic algorithm called FGA (fast genetic algorithm). The code is by Alessandro Presta and claims to be


a simple yet powerful implementation of a general genetic algorithm, and provides many types of crossover and selection procedures. The library is easy to incorporate into other applications. Further improvements include parallelization of the algorithm in multi-processor environments and general performance optimizations.

See here for more detail.

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