This paper proposes an experimental analysis on the convergence of evolutionary algorithms (EAs). The effect of introducing chaotic sequences instead of random ones during all the phases of the evolution process is investigated. The approach is based on the substitution of the random number generator (RNG) with chaotic sequences. Several numerical examples are reported in order to compare the performance of the EA using random and chaotic generators as regards to both the results and the convergence speed. The results obtained show that some chaotic sequences are always able to increase the value of some measured algorithm-performance indexes with respect to random sequences. Moreover, it is shown that EAs can be extremely sensitive to different RNGs. Some t-tests were performed to confirm the improvements introduced by the proposed strategy.

Chaos and random signals share the property of long term unpredictable irregular behavior and broad band spectrum. The aim of this paper is not to distinguish between random and chaotic dynamics, nor to show the use of chaos, but to focus attention on how chaos and noise help order to arise from disorder It means to investigate the effect of the introduction of either deterministic chaotic or random sequences in different types of phenomena. In particular the results related to different applications, self-organization in arrays of locally coupled systems in which a chaotic dissymmetry is present, chaos driven optimization strategies, pattern formation in Drosophila embryos and some new topics on game theory are treated with the aim to investigate the subject: "does chaos work better than noise?".

The methodology proposed in the paper applies artificial intelligence (AI) techniques to the modeling and control of some climate variables within a greenhouse. The nonlinear physical phenomena governing the dynamics of temperature and humidity in such systems are, in fact, difficult to model and control using traditional techniques. The paper proposes a framework for the development of soft computing-based controllers in modern greenhouses