This qualitative research permitted to explain the quantitative study findings and to explore the evolutionary and complex implementation process of PP.
It is worth mentioning that, due to its simplicity in implementation, evolutionary algorithms, such as genetic algorithm and their variants, are extensively utilized for identifying unknown parameters of systems biology models [ 1, 11, 20- 22].
In the evolutionary optimization, two implementations of the objective function, based on a numeric-valued and an interval-valued network error, respectively, are discussed and compared.
Also highlighted are recent hotspots in this area, including the cloud and MapReduce-based implementations, GPU and CUDA-based implementations, distributed evolutionary multiobjective optimization, and real-world applications.
In this paper, the implementation of evolutionary neurocontrol is outlined and its performance for the given problem is shown.
This paper presents a general-purpose software framework dedicated to the design and the implementation of evolutionary multiobjective optimization techniques: ParadisEO-MOEO.
This is accomplished by an implementation of evolutionary programming that explores the available configuration space formed by powers of individual components and inter-component separations to obtain globally or quasiglobally optimum solutions for the problem.
A new implementation of Evolutionary Strategies presented in this paper offers transparent parallelization and, by exploiting common features, offers the prospect of including functionality of Genetic Algorithms, thereby reducing the overall code complexity and size.
The three systems discussed, NED, LMS, and EMDS, are typical of recent approaches to system design insofar as each has taken an evolutionary approach to system implementation in order to develop effective, integrated decision support for forest management in this new, complex problem domain.
