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Can anyone provide a comparison of this book to Nocedal and Wright's book?
Alg4Opt covers more topics, providing the motivation behind the algorithm, sometimes a basic derivation, and a concrete implementation. It has citations in the margin for more info.
Nocedal and Wright will go more in-depth on derivation, proving theorems, etc. Implementations are pseudocode, and fewer topics are covered.
I'm astonished and also disappointed to see that the book has dedicated sections for the metaheuristics Firefly and Cuckoo Search. I don't know what happened there, but any experienced researcher from the field knows that these metaheuristics (among several others) are not serious and are very criticized in the community. There is even a paper in ITOR about this: https://onlinelibrary.wiley.com/doi/abs/10.1111/itor.1…
Can anyone provide a comparison of this book to Nocedal and Wright's book?
This book provides a high level overview of many methods without (on a quick skim) really hinting at the practical usage. Basically this reads as a encyclopedia to me, whereas Nocedal and Wright is more of an introductory graduate course going into significantly more detail on a smaller selection of algorithms (generally those that are more commonly used). Picking on what I'd consider one of the major workhorse metho…
(but better)
anyone has a mirror?
Earlier quoted context omitted.
I think from what you say that you are not familiar with the content of the No Free Lunch theorem. It says that averaging over all possible objective functions that no optimization algorithm is better than median. If you don't know anything about the objective, then the probability that Timefold is better than a randomly chosen optimization algorithm is 50%. The key is the point about averaging over all possible obje…
I thought "No Free Lunch Theorem" was a joke from its name (although I should know better since "Hairy Ball Theorem" exists). So if I understand correct, it states that all optimization algorithm must choose an order to evaluate solutions, and the faster it evaluates the "best" solution, the "better" the algorithm. For example, say the search space is {"a", "b", "c"} and the best solution is "c". Then there are 3! (6…