Abstract
A genetic algorithm used to design radio-frequency binary-weighted differential switched capacitor arrays (RFDSCAs) is presented in this article. The algorithm provides a set of circuits all having the same maximum performance. Thus article also describes the design. implementation. and measurements results of a 0 25 mu m BiCMOS 3-bit RFDSCA. The experimented results show that the circuit presents the expected performance up to 40 GHz. The similarity between the evolutionary solutions, circuit simulations, and measured results indicates that the genetic synthesis method is a very useful tool for designing opitmum performance RFDSCAs (C) 2010 Wiley Periodicals, Inc Microwave Opt Technol Lett 52, 629-634, 2010. Published online in Wiley InterScience (www.interscience.wiley.com) DOI 10.1002/mop.25009

Abstract
In practice, there are several processes which are exhibiting oscillatory behaviour. Some representatives are disk-drive heads, robot arms, cranes and power-electronics. One of techniques, aimed at reducing the oscillations, is Posicast Input Command Shaping (PICS) method. The paper combines the PICS method and Magnitude Optimum Multiple Integration (MOMI) tuning method for PID controllers. The combination of both methods significantly improves the speed and stability of the closed-loop tracking responses. Moreover, the proposed approach is relatively simple for implementation in practice and can be used either on process time-response data or on the process model in frequency-domain.

Abstract
This paper investigate the fractional-order dynamics during the evolution of a Genetic Algorithm (GA). In order to study the phenomena involved in the GA population evolution, themutation is exposed to excitation perturbations during some generations and the corresponding fitness variations are evaluated. Three similar functions are tested to measure its influence in GA dynamics. The input and output signals are studied revealing a fractional-order dynamic evolution.

Abstract
An adaptive greenhouse climate controller was implemented to regulate the air temperature, humidity and carbon dioxide concentration, with the aim of achieving set-point accuracy and reduce energy consumption. An optimization algorithm, based on the minimization of a cost function, was used to tune a Proportional-Integral-Derivative controller. The cost function is computed over a future time horizon of one hour as a function of the errors between the predicted and desired outputs and the predicted energy demand. Since the controller must be able to predict the greenhouse climate, it was needed to employ recursive identification algorithms to estimate in real-time the parameters of the climate model. When compared with commercially available controllers, this adaptive controller proved to have better performance regarding set-point accuracy and energy consumption.

Abstract
The technique of genetic algorithms (GAs) is proposed as a means of auto-tuning PI Smith predictor controllers. The technique involves firstly using on-line data and the GA to identify a model of the process. Then the identified model, the GA and simulation methods, are used to off-line tune the PI Smith predictor controller, so as to minimise a time-domain based cost function. Finally, the genetically tuned controller is implemented on-line on the real process. The results of the auto-tuning of the PI Smith predictor controller technique are illustrated on a laboratory heat exchanger, and a comparison between this technique, and both the genetic PI auto-tuning technique and the Astrom PI auto-tuning technique are made.

Abstract
Artificial co-evolutionary techniques are proposed in a new and novel paradigm to solve the problem of designing a robust fixed PID controller for a plant with prescribed plant uncertainties. The co-evolutionary scheme used, involves generating two separate populations, one representing the controller and the other the plant. Two separate cost functions are then used in the co-evolutionary scheme to reflect the different goals of the two populations. The two populations are then co-evolved such that the population of plants, with the prescribed uncertainties, contains the set of difficult plants to control and a population of controllers emerges, which can control all these difficult plants effectively. The resulting paradigm not only results in a robust controller design but also produces a set of worst case plants. This co-evolutionary approach is illustrated through co-evolving a PID controller for a linear plant which has a set of prescribed uncertainties.