Informal Framework Definition
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*Additional input welcome*

I concieve of the framework for a genetic algorithm package encompassing three 
basic concepts:
    agents
    one or more populations
    one or more environments
    
Agent:
    An agent is the entity which exists in a certain selective environment, as
    a member of a population. Agents may be composed of the following attributes
        - genetic makeup
        - fitness (as determined by the environment)
        - age
    The end use should have direct influence on the structure of the genetic
    make-up of the agents.  
    
Populations:
    A population is composed of agents.  A population is basically a means of 
    organizing agents into the same gene pool for cross breeding purposes.  
    A population may also contain further suborganization, such as familial 
    clusters.
    
    The end-user should be able to determine the following attributes of the
    population:
        - population size
        - familial cluster size (?)
    
Environment:
    An environment is, in a sense, the "world" definition in which one or more
    populations of agents exists.  It is the environment which determines how
    to evaluate the fitness of an agent.  For example, a fish is extremely fit 
    to exist in an ocean, but the same fish will die in a gaseous environment.
    So, it can be said that the environment determines fitness, not the fish.
    
    Furthermore, the environment determines how the genetic makeup of an agent
    will be modified.  The idea being that different environments can have 
    different evolution mechanisms, even if they have the same "fitness" 
    evaluation.  Or, possibly, the fitness evaluation involves many complex 
    components which are difficult to assess all at once, but can be bread
    separately in different environments, and cross bread later for the optimal 
    solution for the combination.

    The end-user should be able to determine the following attributes of the 
    environment:
        - fitness evaluation
        - maximum age of agents in population before death (or specify no death)
        - mutation frequency (both number of mutations to occur, and the chance
          of mutation success.)
        - crossover frequency
        - nudge frequency (temporary high frequency mutation rate to push out of 
          local optima)
     
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Other Entities:

World
    The world class is an interface or facade into the rest of the package.  The
    user input (be it from command line, properties file, or run time) is supplied
    to the World.  A World can contain mutliple envronments, multiple populations,
    etc.  The world object populates the original populations, manages generation
    advancement, etc.
    
Evolve
    A module which will embed the basic evolutionary modification functions that
    are required to "evolve" an agent.  Such as mutate, crossover, catastrophy, etc.
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Applied to a Problem:
    
    Define problem
    Define agents
    Define fitness evaluation
    Define environment parameters (population size, mutation frequency, etc.)
    Build initial population via random sampling
    Evolve population
    Check solution/fitness