Evolving Grid Computing Optimization Through Simulated Evolution (Updated)
Interesting stuff I got via my webnews service: Evolving towards the future of science: genetic algorithms and grid computing.
The main objective has to do with the optimization of job scheduling on a computing grid. Through this, lots of valuable resources are saved, such as energy consumption, CPU time, human resources, and so on.
It seems that the genetic algorithm (GA) applied is the good old CGA or Canon Genetic Algorithm (likely holding some slight modifications, for instance elitism, stochastisc tournament selection, etc.), what stands for bit flip mutation, one point crossover and a fitness based selection scheme. Of course, I may be wrong about that, since I have not read nothing (except that news) upon the genetic algorithm they use for that task.
From the article:
"Standing for Distributed Optimal GENEtic algorithm for Grid applications Scheduling, DIOGENES quickly determines the most efficient way to schedule of a set of jobs on a computing grid, optimizing both time and resources in the process."
I have noticed an interesting (strange?) phenomenon which has taken place in the, let's say, New Wave Of Genetic Algorithms (NWOGA): It seems as though no one is applying them! From my surveys inside the Internet and looking for nice real world applications of evolutionary algorithms, what I have noticed is that when it comes to genetic algorithms, the most applied one still is the... Canon Genetic Algorithm! Surely, as stated above, that CGA which is being applied to solve a particular problem, very frequently, holds modifications, be these on the selection scheme, the evolutionary operators (mutation, crossover, and, from time to time, inversion), population size, or even on the probability tunning of mutation and crossover. The fiddling CGA game has generated, sometimes, strange algorithms.
By the way, I have nothing against NWOGA.
Our blog friend, Julian Togelius, wrote an interesting observation upon that situation. I consider that he aimed at the right target, see:
"I think the reason so few people are using other types of evolutionary algorithms than standard GAs is that few people know of, or understand, anything else. It's really amazing how little people know of what's going on next doors to their own little research field.
So, the people who are busy coming up with new algorithms don't have much time for learning about applications, and vice versa..."
The big academic/technical organizations/publishers (ACM, IEEE, Springer, etc.) could sponsor more crossover events in which different research areas could come a little closer to share their respective experiences, new ideas and so on trying to find common points so that one area could help the other. Perhaps, that attitude could bring more open air to both fields.
P.S: To better understand why I wrote NWOGA, please, see here. :)
Update: Also via MEDAL Blogging.