IlliGAL Blogging

Dr. Goldberg,

In "Genetic Algorithms are not Function Optimizers", "Part 7 Summary and Conclusions", DeJong wrote:

"
The intent of this paper is not to question the usefulness of GAs as function optimization techniques. In fact, the intent is quite the opposite. GA-based function optimizers have already demonstrated their usefulness over a wide range of difficult problems . . ."
"

Unfortunately, DeJong's paper is apparently misunderstood on this simple tenet.

I share your sentiment on usefulness, not just in GAs, but in mathematics and other disciplines.

I find many studies on the undecidable, uncomputable, etc. annoying. This is not because I do or do not believe them, but rather that I feel that humankind is in its infancy in intelligence and discovery. I'd be curious to see what the perspectives are a few hundred years from now when humankind has learned and grown.

I'm in the process of reading TDOI. This is a great summer reading book. I find that not every summer is filled with reading that is this engaging. I'm taking my time with this book as it covers a great deal and there are many simple insights that provide great thought experiments.

Thanks for sharing your positive GA experiences and what CAN be.

---O
 

Hallo!!

Very interesting the blog "Pensive Pondering".

I agree with Dr Goldberg and I think that the NFL and GAFO are much more a stimulus than a worry, because, as NFL states, there is a set of problems that some non-GA/EA can solves well, but, at same time, we need to remember that there are other problems that those same algorithms can't solve or give, at least, a good answer, so here it is a chance to use EA's. The GAFO is another interesting situation, because if we compare the results obtained by classical optimization methods (Research Operations, Monte Carlo, Hillclimbing, etc), we note that EA's, in almost all functions, show us solutions that are, at least, equal in quality when compared to solutions that those classical optimizers offer us.

And as we, in this post, are seeing GA's using a critic view, let me point an interesting article about the subject:

"How GAs do NOT Work: Understanding GAs without Schemata and Building Blocks (1995)" - Hans-Georg Beyer

That article was refused at ICGA of 1995. I think that the reason for that was the so different ideas that tried to explain GA's by another point of view and I think that the persons who analysed Beyer's article liked the things that Dr Beyer was criticizing and were not so happy with the critics, so, it is not so strange that the article had been refused(this is my opinion). The german EA school (Evolution Strategies or ES) has some critics about the three main ideas that, so far, "explain" how GA's work:

1-Schemata
2-Schema Theorem
3-Building Block Hypothesis(BBH)

The germans, and some persons from GA/EA community, say that those three ideas show theoretical aspects not found in practical experiments(see, e.g., Bäck, Michalewicz, Schwefel, Rechenberg, Beyer). They have also some critics about the use of binary codification in GA's(because if your binary string is not so much long, so, you can miss some optimal sample) and show their reasons to use real codification in GA's or to just apply an ES to the problem. There are some critics about GA's as function optimizers, but what the critics do not remember is that the SGA does not solve too much problems and they forgot the origins of GA's, at least as Dr Holland thought about GA's and its applications in adptive systems. What I find so much strange is that, as the SGA does not solve so much problems, its supporters began to make some kind of Dr Moreau-like (or Frankstein-like) experiments with SGA, that is, those supporters tried a lot of new (strange?) things with the SGA, for example: Messy-GA, Greedy-GA, Delta Coding and etc. Some names could be used very well for some Horror/Terror movies. I am not saying that we do not need to try some new stuffs in GA, but what we can't forget is the original roots of GA's, that is, we need to try some modifications, but, at same time, we need also to be very careful not to build some kind of monster that, as it is "very useful" and satisfies the researcher's expectations, could be useful for nothing more and less useful for real world problems. I think that Evolution Strategies(ES) followed a very different path in its evolution, because if you see ES's have not diverged so much from the original idea. "O.k!", someone could tell me, "but you must admit that ES's were very applied only in Germany almost until the 1990's and the GA's had a range of researchers bigger than ES, so, it is not strange that there are in GA those 'monsters' that you, Nosophorus, says". I totally agree, but we should remember also that ES were built in a very interesting base and the key-concepts(global convergence, self-adaptation) of it are very useful to optimization problems.

I am not trying to find which is better, ES or GA, I think that both are very useful(and in some situations, GA's and ES's have not so much differences). I like so much GA and I think that it has a great future in its development, but you should admit that ES are very useful too.

Anyway, GA's and ES's are great evolutionary algorithms, however I think that ES's are much more elegant. :D

[]´s

Marcelo(a.k.a Nosophorus)
 

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