Why does Chaos Improve Evolutionary Algorithms?

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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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		Pseudo versus True Randomness (PRNGs versus TRNGs) Jaap de Jonge, Editor, Netherlands
	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 Pseudo-Random 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

		Evolution and Devolution Resulting from Chaos Prof. Arup Barman, Professor, India, Premium Member
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