Abstract
This paper addresses the problem of finding several different solutions with the same optimum performance in single objective real-world engineering problems. In this paper a parallel robot design is proposed. Thereby, this paper presents a genetic algorithm to optimize uni-objective problems with an infinite number of optimal solutions. The algorithm uses the maximin concept and e-dominance to promote diversity over the admissible space. The performance of the proposed algorithm is analyzed with three well-known test functions and a function obtained from practical real-world engineering optimization problems. A spreading analysis is performed showing that the solutions drawn by the algorithm are well dispersed. © 2013, Springer Science+Business Media Dordrecht.

Abstract
Particle swarm optimization (PSO) is a search algorithm inspired by the collective behavior of flocking birds and fishes. This algorithm is widely adopted for solving optimization problems involving one objective. The evaluation of the PSO progress is usually measured by the fitness of the best particle and the average fitness of the particles. When several objectives are considered, the PSO may incorporate distinct strategies to preserve nondominated solutions along the iterations. The performance of the multiobjective PSO (MOPSO) is usually evaluated by considering the resulting swarm at the end of the algorithm. In this paper, two indices based on the Shannon entropy are presented, to study the swarm dynamic evolution during the MOPSO execution. The results show that both indices are useful for analyzing the diversity and convergence of multiobjective algorithms.

Abstract
The challenge of noise attenuation in images has led to extensive research on improved noise reduction techniques, preserving important image characteristics, improving not only visual perception, but also enabling the use for special purposes, such as in medicine to increase clarity of medical images. In this paper, a technique for noise attenuation in medical images is proposed. Its operation takes place through the application of an adapted genetic algorithm. The results of experiments show that the proposed approach works best in suppressing artifacts and the preservation of the structure compared with several existing methods.

Abstract
A significant number of search and optimisation techniques whose principles seek inspiration from nature and biology phenomena have been proposed in the last decades. These methods have been successfully applied to solve a wide range of engineering problems. This is also the case of greenhouse environment control, which has been incorporating this type of techniques into its design. This paper addresses evolutionary and bio-inspired methods in the context of greenhouse environment control. Algorithm principles for reference techniques are reviewed, namely: simulated annealing, genetic algorithm, differential evolution and particle swarm optimisation. The last three techniques are considered using single and multiple objective formulations. A review of these algorithms within greenhouse environment control applications is presented, considering single and multiple objective problems, as well as their current trends.

Abstract
Bioinspired search algorithms are widely used for solving optimization problems. The evolution progress isusially measured by the fitness value of the population fittest element. The search stops when the algorithm reaches a predetermined number of iterations, or when no improvement is achieved after some iterations. Usually, no information, behind the best global objective value, is fed into the algorithm to influence its behavior. In this paper, a entropy metric is proposed to measure the algorithm convergence. Several experiments are carried out using a particle swarm optimization to analyze the metric relevance. Moreover, the proposed metric is used to implement a strategy to prevent premature convergence to suboptimal solutions. The results show that the index is useful for analyzing and improving the algorithm convergence during the evolution. © 2020, Springer Nature Switzerland AG.

Abstract
This work presents a neural network used to diagnosis patients with benign or malignant breast cancer. The study is carried out using the Breast Cancer Wisconsin dataset. To solve the problem a feedforward neural network (NN) with multilayers was used. In the work, the implementation was made in Python, using two different libraries (sklearn and keras). Experimental results were obtained by performing simulations in both developed applications, and the performance of the neural classifier was evaluated through the performance measures of the classification systems and the ROC curve. The results were promising, since the NN was able to discriminate with high accuracy the two separable sets discriminating the benign or malignant tumor patients.