REGAL is publicly availble code.
 
REGAL (RElational Genetic Algorithm Learner)
is a distributed genetic algorithm-based system, designed
for learning multi-modal First Order Logic concept descriptions
from examples.

REGAL is based on a selection operator, called Universal Suffrage
operator, provably allowing the population to asymptotically
converge, in average, to an equilibrium state, in which several
species coexist.

REGAL makes use of PVM 3.3 and Tcl/Tk.
This version of REGAL is provided with a graphical user interface
developed in Tcl/Tk language.
 
REGAL has been jointly developed by:
 
Attilio Giordana at University of Torino, Dipartimento di Informatica, Italy.
e-mail:  attilio@di.unito.it
URL:     http://www.di.unito.it/~attilio/
 
Filippo Neri at University of Torino, Dipartimento di Informatica, Italy.
e-mail:  neri@di.unito.it
URL:     http://www.di.unito.it/~neri/
 
Version:      3.2
 
Requires:     C, PVM3.
 
References:
 
 1. Neri  F.  and Giordana A. (1995). "A Distributed  Genetic
 Algorithm for Concept Learning", Proc. Int.  Conf.  on
 Genetic Algorithms  (Pittsburgh, PA), Morgan Kaufmann, pp. 436-443.
          
 2. Neri  F.  and  Saitta L. (1995). "A Formal  Analysis  of
 Selection  Schemes".  Proc.  Int.  Conf.  on   Genetic
 Algorithms  (Pittsburgh,PA), Morgan Kaufmann, pp. 32-39 .

 3. Giordana A. and Neri F. (1996). "Search-Intensive Concept
 Induction". Evolutionary Computation
 Journal, MIT Press, vol. 3, n. 4, pp. 375 - 416.
                            
 4. Neri F. and  Saitta L. (1997). "An Analysis of the
 Universal Suffrage Selection Operator". Evolutionary Computation
 Journal, MIT Press, vol. 4, n. 1, pp. 89-109.
 
 5. Neri F. and  Saitta L. (1996). "Exploring the Power of Genetic
 Search in Learning Symbolic Classifiers". IEEE Transaction
 on Pattern Analysis and Machine Intelligence, IEEE Computer
 Society, to appear.