These can only be quantified using intelligent computational techniques such as evolutionary computation and fuzzy logic.
Stochastic approaches such as evolutionary algorithms have been widely used in various science and engineering problems.
This property may be prejudicial for optimization methods such as evolutionary algorithms (EA) which require some diversity during the search.
Population-based sampling methods, such as evolutionary algorithms, are generally applied to solve large optimization problems with rugged energy landscapes.
As the processors getting faster, cheaper, and smaller the question arises: is it possible to utilize costly algorithms such as evolutionary algorithms in such a real-time application?
Today, this process can be automatized using stochastic, nature-inspired, population-based algorithms, such as evolutionary and swarm intelligence-based algorithms.
Stochastic search techniques such as evolutionary algorithms (EA) are known to be better explorer of search space as compared to conventional techniques including deterministic methods.
The use of machine learning techniques, such as evolutionary algorithms or artificial neural networks for the learning of these controllers allows to automate the design process.
Being a rapidly growing area, new trends, such as evolutionary game theory, in understanding and design of large scale Optical Network are also identified and discussed.
Such toy models are intended to capture features such as evolutionary trends, coupled evolutionary dynamics of phenotype and genotype, adaptive change, branching, and evolutionary transience.
One of the approaches to combinatorial optimization is to use global search methods, such as evolutionary algorithms combined with local search procedures.
