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非常好的进化算法EC 实现平台 可以实现多种算法 GA GP

+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+

SPAM e-mail database (spambase): Machine learning using strongly-typed GP
with Open BEAGLE

Copyright (C) 2001-2003
by  Christian Gagne <[e-mail]>
and Marc Parizeau <[e-mail]>

+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+


Getting started
===============

  Example is compiled in binary 'spambase'. Usage options is described by
  executing it with command-line argument '-OBusage'. The detailed help can
  also be obtained with argument '-OBhelp'.

Objective
=========

  Find a program the will successfully predict whether a given e-mail is spam
  or not from some extracted features.

Comments
========

  The evolved programs works on floating-point values AND Booleans values.
  The programs must return a Boolean value which must be true if e-mail is
  spam, and false otherwise. Don't expect too much from this program as
  it is quite basic and not oriented toward performance. It is there mainly
  to illustrate the use of strongly-typed GP with Open BEAGLE.

Terminal set
============

  IN0, IN1, ...  up to IN56, the e-mail features.      [floating-point]
  0 and 1, two Boolean constants.                      [Boolean]
  Ephemeral constants randomly generated in $[0,100]$  [floating-point]

Function set
============
  AND               [Inputs: Booleans,        Output: Boolean]
  OR                [Input:  Boolean,         Output: Boolean]
  NOT               [Inputs: Booleans,        Output: Boolean]
  +                 [Inputs: floating-points, Output: floating-point]
  -                 [Inputs: floating-points, Output: floating-point]
  *                 [Inputs: floating-points, Output: floating-point]
  /                 [Inputs: floating-points, Output: floating-point]
  <                 [Inputs: floating-points, Output: Booleans]
  ==                [Inputs: floating-points, Output: Booleans]
  if-then-else      [1st Input: Boolean, 2nd & 3rd Input: floating-points,
                     Output: floating-point]

Fitness cases
=============

  A random sample of 400 e-mails over the database, re-chosen for
  each fitness evaluation.

Hits
====

  Number of correct outputs obtained over the 400 fitness cases.

Raw fitness
===========

  Ignored (always 0).


Standardized fitness
====================

  Rate of correct outputs over the fitness cases where
  the desired output was 0 (non-spam).

Adjusted fitness
================

  Rate of correct outputs over the fitness cases where
  the desired output was 1 (spam).

Normalized fitness
==================

  Rate of correct outputs obtained over all the 400 fitness cases.

Stopping criteria
=================

  When the best individual scores 400 hits or when the evolution reaches
  the maximum number of generations.

Reference
=========

  Machine learning repository,
  http://www.ics.uci.edu/~mlearn/MLRepository.html