Abstract
This paper presents a hybrid genetic algorithm, also known as Memetic Algorithm (MA), applied to the design of combinational logic circuits. In view of the fact that hybrid algorithms have shown to be very effective in solving many hard combinatorial optimization problems, the proposed MA combines a Genetic Algorithm (GA) for digital circuit design with the gate type local search (GTLS). The combination of a global and a local search is a strategy adopted by recent hybrid optimization approaches. The main idea is to apply a local refinement to an Evolutionary Algorithm (EA) in order to improve the fitness of the individuals in the population. The results show an improvement of the final fitness function followed by a reduction of the average number of generations required to reach the solutions and its standard deviation, for all the tested circuits.

Abstract
This paper analyzes the performance of two cooperative robot manipulators. It is studied the implementation of fractional-order algorithms in the position/force control of two robots holding an object. The experiments reveal that fractional algorithms lead to performances superior to classical integer-order controllers.

Abstract
Fractional calculus (FC) was originally developed in a pure mathematical viewpoint. However, nowadays FC is applied in many emerging fields of physics and engineering. This paper studies the fractional electrical impedance of vegetables and fruits having FC modelling in mind. In this line of thought, are developed several experiments for measuring the impedance of botanical elements, based in the Bode and polar diagrams. An electrical circuit that models these systems is presented and conclusions are drawn. Copyright © 2006 IFAC.

Abstract
This paper analyses the performance of a Genetic Algorithm (GA) in the synthesis of digital circuits using a new approach. The novel concept extends the classical fitness function by introducing a fractional-order dynamical evaluation. The dynamic fitness function results from an analogy with control systems where it is possible to benefit the proportional algorithm by including a differential component. For this purpose the non integer derivative is approximated through Padé fractions. The experiments reveal superior results when comparing with the classical fitness method. Copyright © 2006 IFAC.

Abstract
This paper proposes a genetic algorithm for designing combinational logic circuits and studies four different/case examples: the 2-to-1 multiplexer, the one-bit full adder, the four-bit parity checker and the two-bit multiplier. The objective of this work is to generate a functional circuit with the minimum number of logic gates. It is also studied the scalability problem that emerges from the exponential growth of the truth table when the circuits complexity increases. Furthermore, it is as well investigated the population size and the processing time for achieving a solution in order to establish a compromise between the two parameters.

Abstract
This study introduces a technique for the design of multivariable decentralized proportional-integral controllers (Pl). This problem is addressed by first analyzing the pairing possibilities of the manipulated and controlled variables, which result in minor interaction between the loops; controller tuning is then performed by using the particle swarm optimization algorithm. The control objective is to design the multivariable controller for the several viable pairing possibilities, minimizing the error between the controller outputs and the set-point variables. Simulation results presented allow the conclusion that the proposed technique can tune the controller with all the loops closed, reducing the interaction effect. It is also shown that for this problem the particle swarm algorithm is faster in converging than a genetic algorithm.