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Bagher Zarei, Lecturer, Iran

I have presented an evolutionary algorithm using chaos theory (chaotic numbers) to solve an optimization problem. The results of the experiments show that this algorithm is much better than the same evolutionary algorithms without chaos theory (random numbers). Why are the chaotic numbers making the results better?
What could be a scientific and compelling reason for this effect? (...) Read more? Sign up for free

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Jaap de Jonge, Editor, Netherlands


Pseudo versus True Randomness (PRNGs versus TRNGs)
Random numbers can be used for several different purposes, such as generating data encryption keys and explaining / simulating / modeling complex phenomena like evolution and many phenomena in the management domain.
When you want use a sequence of random numbers, each number drawn has to be statistically independent of the others.
According to Dr Mads Haahr at random.org, in randomness one has to distinguish pseudo from true (=chaotic) randomness and also pseudo from true (=chaotic) random number generators (PRNGs from TRNGs).
The characteristics of TRNGs have been found to be quite different from PRNGs:
Characteristic 
PseudoRandom Number Generators 
True Random Number Generators 
Efficiency 
Excellent 
Poor 
Determinism 
Deterministic 
Nondeterministic 
Periodicity 
Periodic 
Aperiodic 
Without being an expert in this field, I suspect the scientific answer to your question (Why chaos improves evolutionary algorithms?) results from the above table.
On a more day to day level, it appears obvious that using chaotic approaches to simulate evolution is superior, since it is common knowledge that evolution is taking place due to chaotic circumstances. It is based on truly random mutations of genes which then sometimes happen to be beneficial for a better chance of survival.
Source: random.org



Prof. Arup Barman, Professor, India


Evolution and Devolution Resulting from Chaos
It is quite an unscientific thinking of the past f (...)




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