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.

Abstract
The aims of the present study were: to identify the factors which are able to explain the performance in the 200 meters individual medley and 400 meters front crawl events in young swimmers, to model the performance in those events using non-linear mathematic methods through artificial neural networks (multi-layer perceptrons) and to assess the neural network models precision to predict the performance. A sample of 138 young swimmers (65 males and 73 females) of national level was submitted to a test battery comprising four different domains: kinanthropometric evaluation, dry land functional evaluation (strength and flexibility), swimming functional evaluation (hydrodynamics, hydrostatic and bioenergetics characteristics) and swimming technique evaluation. To establish a profile of the young swimmer non-linear combinations between preponderant variables for each gender and swim performance in the 200 meters medley and 400 meters font crawl events were developed. For this purpose a feed forward neural network was used (Multilayer Perceptron) with three neurons in a single hidden layer. The prognosis precision of the model (error lower than 0.8% between true and estimated performances) is supported by recent evidence. Therefore, we consider that the neural network tool can be a good approach in the resolution of complex problems such as performance modeling and the talent identification in swimming and, possibly, in a wide variety of sports.

Abstract
Parallel manipulators have attracted the attention of researchers from different areas such as: high-precision robotics, machine-tools, simulators and haptic devices. The choice of a particular structural configuration and its dimensioning is a central issue to the performance of these manipulators. A solution to the dimensioning problem, normally involves the definition of performance criteria as part of an optimization process. In this paper the kinematic design of a 6-dof parallel robotic manipulator is analyzed. Three performance criteria are formulated and optimal solutions are found through a particle swarm formulation.

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
This work addresses the fractional-order dynamics during the evolution of a Genetic Algorithm population (GA) for generating a robot manipulator trajectory. The GA objective is to minimize the trajectory space/time ripple without exceeding the torque requirements. In order to investigate the phenomena involved in the GA population evolution, the mutation is exposed to excitation perturbations and the corresponding fitness variations are evaluated. The input/output signals are studied revealing a fractional-order dynamic evolution, characteristic of a long-term system memory.

Abstract
A new optimisation algorithm is proposed that results from merging a split adaptive particle swarm optimisation algorithm with the differential evolution algorithm. The proposed technique adopts the crossover type of operator used within the differential evolution algorithm to update the swarm particle velocity vectors. The hybrid algorithm is tested in a well known benchmark continuous function and its performance compared with the particle swarm optimisation and differential evolution algorithms. Copyright (C) 2003 IFAC.