Abstract
The design of robotic manipulators considering both the structure and trajectory planning problems is addressed. These problems have been solved using either classical or metaheuristics optimization techniques considering single objectives. This paper proposes a multi-objective evolutionary algorithm to generate the robot structure and required manipulator trajectories. The proposed evolutionary algorithm is organized in a hierarchical form by using three genetic algorithms to optimize the initial, final and intermediate robotic configurations which are executed for each population member of top level multi-objective structure generator. The aim is to minimize the trajectory space ripple, the initial and final binary torques, while optimizing the mechanical structure. Simulations results are presented from solving a structure synthesis problem which considers the optimization of three simultaneous objectives.

Abstract
A set of modern heuristic techniques is reviewed in the context of PID control structures optimization. The selected techniques are: simulated annealing, genetic algorithm, population based incremental learning algorithm, particle swarm optimization algorithm and the differential evolution algorithm. An introduction to each algorithm is provided followed by an illustrative example based in a simulation assignment of an evolutionary algorithms course. Some conclusions are presented about the effectiveness of the reviewed heuristics based on the simulation results.

Abstract
This paper proposes and analyses the performance of a Genetic Algorithm (GA) using two new concepts, namely a static fitness function including a discontinuity measure and a fractional-order dynamic fitness function. The GA is adopted for the synthesis of combinational logic circuits. In both cases, experiments reveal superior results in terms of speed and convergence to achieve a solution.

Abstract
Fuzzy modelling has been widely applied as a powerful methodology for the identification of nonlinear systems from process measurements. Most applications use flat sets of fuzzy rules, which are hardly interpretable black-box approaches. Hierarchical modelling is a promising tool to deal with real world complex systems. A large-scale model can be easily readable if it is partitioned into several independent smaller models to represent functional relations of the processes involved in the system. This article deals with the application of a new fuzzy modelling technique that automatically organizes the sets of fuzzy IF-THEN rules in a Hierarchical Collaborative Structure. This organizational structure makes the fuzzy model interpretable as in the case of the physical model. This new methodology was tested to split the inside greenhouse air temperature and humidity flat fuzzy models into fuzzy sub-models. which have alike counterpart on the physical sub-models.

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 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.