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人智算法基本程序

				  Genetic Algorithm Program

Welcome to the wonderful world of the Genetic Algorithm C++ directory.
This program demonstrates having a population of individuals, each of
whom is given a fitness value. The entire population evolves through
mutation and crossover to maximize the fitness function.  The program
is meant to be a companion to chapter 4 of the "Artificial
Intelligence" textbook. This code was written by Jon Monsarrat,
[e-mail].

Five sections describe the program:

1. The problem
2. Building the program
3. Running the program
4. The solution, and how to read the source code

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1. The problem

The book goes into quite some detail about the problem, but here it is
in a nutshell.

We want to maximize some fitness function that applies to a bunch of
boolean values. This bunch we call a chromosome, and each boolean
value is an allele. Given a population of randomly generated
chromosomes, we want to evolve a new generation out of the fittest of
the current population.

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2. Building the program

The source code in this directory is complete and does not depend upon
any other source code from the textbook. It only uses standard
and libraries which should come any C++ compiler.

You can build this program in UNIX by simply typing

      make

You may need to edit the Makefile to change the name of the compiler,
if you do not have a compiler named "CC".

You will need to recompile the program if you want to add your own
fitness measuring functions. The program comes with two example
functions so you can so try it out.

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3. Running the program

Running the program is very simple. Just type

   genetic -single

to try to maximize the single_func() function for 10 generations of evolution.
More generally, you can type

   genetic <-multiple> <-single> <-a num> <-i num> <-g num>

     -multiple   Evolve to maximize the multiple function
     -single     Evolve to maximize the single function
     -a num      Use num many alleles in each individual
     -i num      Use num many individuals in the population
     -g num      Use num many generations of evolution


There is no example filename. Because the fitness functions are C++
functions, you will have to recompile the prorgam to use your own
fitness function.
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4. The solution, and how to read the source code

This solution is quite close to the Lisp version given in the textbook.

The classes are:

   Population -- Holds the entire population of chromosomes.
   Chromosome -- Holds a single chromosome, with its alleles.

There are 2 source files in this directory:

   Population.C  The Population class and the main() function
   Chromosome.C  The Chromosome class

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