Genetic Algorithms in Swarm

[Ferraris]

        Hi all
        
        I've read messages from Sid Bhattacharyya and Paul E. Johnson about 
swarm agents with genetic algorithms. Using my poor english, for which I beg 
your pardon, I'll try to describe an experimentation I've done during working 
on GM. I have written a small model in which the  GA has to find the maximum of 
the function:
             y = 1 + cos(x) / (1 + 0.01 * x ^ 2)

In my model each individual encode a natural number that is translated to a 
real value in an assigned range as follow:

        x = a + n * 1/((2 ^ l - 2)/b)

where a and b are the minimum and maximum values of the inspected range of the 
domain, n is the number obtained reading each individual, i.e. each sequence of 
"0" and "1",  as binary number and l is the length of each individual. Then the 
function is computed and the result is given to the individual as fitness 
value.  

        Using the "useDeltaFitness" algorithm coded in GM, I have got good 
results: the GA was more precise of a traditional one. Using GM the faature 
enabling simpy consists in setting to one the useDeltaFitness parameter in the 
probeMap of the ModelSwarm. More about that is contained in the how-to file. 
This model is not included in the distribution file of GM, but if anyone likes 
have the model I'll pleased to send it to him. 

        The distributed SwarmModel of GM is based on the individual approach of 
genetic algorithm, i. e. each individual of the GA is like an agent, but the 
kernel of GM is able to act as well following a mind-approach, i. e. the 
individuals of the GA are like ideas into the brain of a single agent and each 
agent have an own population. Because the kernel is able to operate on several 
population, in fact each agent can have an own GA.This approach is followed in 
CW for classifier systems. For more information please mail me. 
        
        About developing GM and CW I'll send as soon as possible a new version 
of them with few additional features. I know the problems arising from using 
software under modification and I promise to take care in preserve 
compatibility with old versions. 

        Actually I'm also trying to realise a unique package to deal both with 
GAs and CSs that will allow cooperation between individuals from different 
population, operating so in a parallel way. This type of approach has been well 
 described by Alba & Troya in the article "A Survey of Parallel Distribuetd 
Genetic Algorithms" in Complexity vol. 4 pages 31-52.  
         
        Thanks for each observation and suggestion 
        
        Gigi

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