How CS has used the island model to extrapolate the design of a model for software/hardware dynamics is beyond my knowledge base. Biology has been my learn'n.
The following is an intuitive guess and may be redundant:
In ecology, an oversimplified island model is a theory that details why diversity varies between islands. Island size is a direct relationship to genotype diversity. Island's distance from the main land and/or other islands is inversely related to the island's diversity. Other factors could be included such as island's global location, climate's seasonal temp/wind/current patterns that may also alter specie migration patterns, human impact, etc. This aforementioned model may include predator prey relationships, inter/intra specie competition for resources, and other food web facts and specie relationships.
At best, a software program will only be as good as the data derived from good field work. Traps that are not properly set will yield poor results, as well as animal behavior not accounted for or known/observed. Field methods may enhance or impair how the target animal behaves. Data may be precise but not accurate. Building a model on limited research might be a waste of computer resources when a less interesting but statistically accurate environment might better merit data mining, to dwell on how to mitigate hardware needs and software design. Where field data produces copious results that fine tune statistical curves, I would think a population could be grouped such that individual calculations could be avoided for each organism when a subroutine wants to find out how many birds with small beaks fail to eat enough food when the weather impairs their food source. However, individual computations may create enhanced results, that is if the average wave height at 60 fathoms forty feet from a shoal on the north side of an island, during high tide in a fixed hector^2 area is found at 2PM when the moon is new and water temp is 27C, the frequency of the rouge wave height can be modeled using RND and an S curve, but it doesn't tell you where this wave occurred in the measured zone. If the birds that die off are individually computed, then other field factors may be altered such as how that bird's death impacts its immediate territory. The bird population may encompass territory shared by species that are niche or mutually exclusive to other species or in defined zones depending on many relationship factors. The death of a bird(s) where it competes for certain resources (including nesting sites) with a niche community may just help (or hurt) that niche community's population, approaching a new population limit which would alter your diversity model and CPU/memory needs. Also, learned behavior in certain animals might not be accounted for in a program, such as a cat that doesn't forage past a boundary of a competitive neighbor (although that neighbor has gone/died) before another competitor takes advantage of the resource.
I would think a good program would divide the model into several programs, some suggestions: [most to least predictable] i) weather, ii) geography, iii) botany, iv) animal profiles excluding prokaryotes unless the animal needs it, v) tags for profile attributes that use similar calculations, vi) uncommon computations, and vii) monitor for CPU/memory needs between programs at n cycle. I imagine the weather modal would easily merit predictable CPU/memory power, as well as the geographic model (physical attributes as well as effects caused by animals/plants/time such as how much nitrate is in soil, hard/soft water, pH, water/soil cohesion tension, etc), and to a less extent the botany model (current plant surveys and known/recent trends). The aforementioned models will be using data that is by far easier to obtain than when compared to animals, so I would find good/meaningful curves to represent these items for the program (so knowing what kind of animal would alter the number of variables needed for a profile). The variable CPU/memory power would most likely need to be fluid when approaching animal relationships. Perhaps generalized curves could be used for populations or regions at n cycle unless an event merits itemizing each organism's profile (ie, approaching a limit where a virus bloom may occur as augmented by an organism's profile, weather, geography).
Some animals are easy to study while others are obscure, so profile variables would need to address the info known about the genotype. Without a keystone organism, the continued computing of profiles for other species in the shared domain would be pointless, so to speak. A library of curves could be built for certain species, so individual profiles may not be necessary when field data is solid. How and by what standards field research is measured would need to be a collective discussion to establish a benchmark as to when a specie population can be statistically infused in the code vs a population with individually computed profiles, or what kind of event would merit itemizing a statistical population into a collection of individual profiles.
Perhaps R species might warrant greater use of statistics vs K species with individual profiles?
If RND generation takes more time than to access when compared to seeking an address holding a preset RND, then I would harvest idle CPU time to stack RND numbers such that when an event requires use of curves or heavy profile calculations, the call uses the stack. [I don't know if this is good application of CS.]
On 3/9/06, Eric Thibodeau <[EMAIL PROTECTED]> wrote:
I happen to be working on just this subject ;)
Island model can be more than one thing, it depends on your implementation.
>From your description, you seem to be implementing a "multiple-deem" approach
while retaining the same GA parameters across these islands. David A. Van
Veldhuizen addresses the parallelisation issues in his document entitled
"Considerations in Engineering Parallel Multiobjective Evolutionary
Algorithms" http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=1197689
Even though his article is oriented towards MOGAs, most if not all issues
brought up in his article also apply to GAs. To give you an idea, here are
four points brought up in his article specifically about island model:
Four basic island pMOEA variants are seen to exist, each with
appropriate migration operators.
1) All islands execute identical MOEAs/parameters (homo-
geneous).
2) All islands execute different MOEAs/parameters (hetero-
geneous).
3) Each island evaluates different objective function subsets.
4) Each island represents a different region of the genotype
or phenotype domains.
My masters is actually geared at trying to "answer" or "reply" to some of the
unanswered questions brought up in this same article (providing quantitative
results with a clearly defined/detailed environment specification as well as
some sort of recipe for parallelising a specifically given MOGA).
I hope this reference helps!
Eric
Le Dimanche 5 Mars 2006 03:30, purnima a écrit:
> hi,
> I've to implement island model genetic algorithm using MPI. the
problem is I've already implemented a parallel Genetic Algorithm using MPI
where i divide population into subpopulations and distribute it 2 all the
processors, and each processors performs sequential GA on its subpopulation
after some iteration these processors migrates its best chromosome toe ach
other and again proceed.
>
> My problem is that i dnt understand what exactly is island model? what
island represents?? how shud i implement it..
> Plz reply soon..
> thanx
>
>
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