A genetic algorithm for job-shop scheduling with operators enhanced by weak Lamarckian evolution and search space narrowing

The job-shop scheduling problem with operators is a very interesting problem that generalizes the classic job-shop problem in such a way that an operation must be algorithm to solve this problem considering makespan minimization. The genetic algorithm uses permutations with repetition to encode chromosomes and a schedule generation scheme, termed OG&T, as decoding algorithm. This combination guaranties that at least one of the chromosomes represents and optimal schedule and, at the samhat machines and operators are idle while an operation is available to be processed. To improve the quality of the schedules for large instances, we use Lamarckian evolution and modify the OG&T algorithm to further reduce the idle time of the machines and operators, in this case at the risk of leaving all optimal schedules out of the search space. We conducted a large experimental study showing that these improvements allow the genetic algorithm to reach high quality solutions in very short time, and so it is quite competitive with the current state-of-the-art methods.     

The effect of adding devulcanized rubber on the thermomechanical properties of recycled polypropylene

This work aims to investigate the effect of adding vulcanized or partially devulcanized rubbers on recycled polypropylene (PPr), considering thermomechanical and morphological properties. The study proposes to better understand how structural changes underwent by rubber (after the devulcanization) contributed to improving the mechanical properties of the PPr. The PPr/rubber blends were prepared by a co-rotating twin-screw extruder and then were injected. The blends composed of the most devulcanized rubbers by microwaves with refined microstructure showed higher values of elongation at break and toughness. Data showed that the devulcanization process applied to the rubber interfered positively in its adhesion to the PPr. Data from dynamic mechanical analysis and atomic force microscopy indicated that the most devulcanized rubbers presented an interface more connected to PPr. These chemical interactions possibly impacted the mechanical properties of the PPr. Moreover, dilatation processes favored the fracture mechanisms of the PPr when rubber was added to it.     

Staphylococcal growth and enterotoxin production in the presence of meat cultures (non LAB)

The effect of meat cultures (non lactic acid bacteria) on the growth and production of enterotoxins and thermonuclease by Staphylococcus aureus was studied. Micrococcus varians did not affect growth nor the synthesis of metabolites. Levels of enterotoxins A, B and D produced by the respective S. aureus strains were reduced by S. xylosus, S. saprophyticus and S. carnosus. The two latter species prevented production of enterotoxin C₁ and S. xylosus markedly reduced the amount produced. The three coagulase-negative staphylococci showed little inhibitory effect on the growth of S. aureus. Penicillium nalgiovense did not show inhibitory activity against the four S. aureus strains. Debaryomyces hansenii slightly inhibited growth of the enterotoxin A-producing strain, but reduced enterotoxin synthesis at 30 °C. Thermonuclease was detected whenever enterotoxins were detected though the influence of the effector organism was dependent on the test strain.     

DipGame: A challenging negotiation testbed

There is a chronic lack of shared application domains to test advanced research models and agent negotiation architectures in Multiagent Systems. In this paper we introduce a friendly testbed for that purpose. The testbed is based on The Diplomacy Game where negotiation and the relationships between players play an essential role. The testbed profits from the existence of a large community of human players that know the game and can easily provide data for experiments. We explain the infrastructure in the paper and make it freely available to the AI community.     

K Nearest Neighbor Equality: Giving equal chance to all existing classes

The nearest neighbor classification method assigns an unclassified point to the class of the nearest case of a set of previously classified points. This rule is independent of the underlying joint distribution of the sample points and their classifications. An extension to this approach is the k-NN method, in which the classification of the unclassified point is made by following a voting criteria within the k nearest points.The method we present here extends the k-NN idea, searching in each class for the k nearest points to the unclassified point, and classifying it in the class which minimizes the mean distance between the unclassified point and the k nearest points within each class. As all classes can take part in the final selection process, we have called the new approach k Nearest Neighbor Equality (k-NNE).Experimental results we obtained empirically show the suitability of the k-NNE algorithm, and its effectiveness suggests that it could be added to the current list of distance based classifiers.     

Evolution of functional link networks

This paper addresses the genetic design of functional link networks (FLN). FLN are high-order perceptrons (HOP) without hidden units. Despite their linear nature, FLN can capture nonlinear input-output relationships, provided that they are fed with an adequate set of polynomial inputs, which are constructed out of the original input attributes. Given this set, it turns out to be very simple to train the network, as compared with a multilayer perceptron (MLP). However finding the optimal subset of units is a difficult problem because of its nongradient nature and the large number of available units, especially for high degrees. Some constructive growing methods have been proposed to address this issue, Here, we rely on the global search capabilities of a genetic algorithm to scan the space of subsets of polynomial units, which is plagued by a host of local minima. By contrast, the quadratic error function of each individual FLN has only one minimum, which makes fitness evaluation practically noiseless. We find that surprisingly simple FLN compare favorably with other more complex architectures derived by means of constructive and evolutionary algorithms on some UCI benchmark data sets. Moreover, our models are especially amenable to interpretation, due to an incremental approach that penalizes complex architectures and starts with a pool of single-attribute FLN     

Agent-Mediated Electronic Commerce

Electronic commerce has been one of the traditional arenas for agent technology. The complexity of these applications has been a challenge for researchers that have developed methodologies, products, and systems, having in mind the specificities of trade, the interaction particularities of commerce, the strict notion of commitment and contract, and the clearly shaped conventions and norms that structure the field. In this paper I survey some key areas for agent technology which, although general, are of special importance in electronic commerce, namely, solid development methodologies, negotiation technologies and trust-building mechanisms. I give examples of systems in which I have directly participated, although I also try to refer to the work of other AgentLink Special Interest Group members over the last few years.     

Structural design of carbon/epoxy bio‐inspired wind turbine blade using fluid/structure simulation

The purpose of this paper is to present the structural design procedure of a low‐speed, horizontal axis, bio‐inspired wind turbine blade made of carbon/epoxy. The methodology initiates with the mechanical characterization of the carbon fiber composite material. An aerodynamic simulation using Computational Fluid Dynamics (CFD) method is performed in order to obtain the pressure distribution profile of the blade. This result is coupled with a Finite Element Analysis (FEA) to carry out an iterative design process through a Fluid‐Structure Interaction (FSI) simulation. Different stacking sequences of laminates are evaluated to find a configuration which allows balance between aerodynamic and dynamic inertial loads, ensuring an almost undeformed geometry during wind turbine's operation. The final structural design of the blade consists in six regions with different laminates. These are balanced and symmetric with distinct thickness characteristics and stacking sequences, which vary in three different orientations: 0^°, ± 45^° and 90^°, achieving a minimum deflection at the tip close to 3.11 cm, and a total weight of 3.6 kg of a 1.8 m radius blade, even with the restrictions imposed by the non‐conventional geometry. Copyright © 2016 John Wiley & Sons, Ltd.     

Comparison of machine learning algorithms for optimization and improvement of process quality in conventional metallic materials

This paper presents a particular problem dealing with the apparition of burr during the drilling process in the aeronautic industry. This burr cannot exceed a height limit of 127?μm as set out by the aeronautical guidelines and must be eliminated before riveting. If this is not performed, it can cause structural damage which would constitute a danger due to the lack of safety. Moreover, the industry needs to find an automated and optimised process in which the drilling and deburring can be carried out in real time, eliminating those other unnecessary tasks, in order to obtain high-quality pieces. The work presents the applicability of data mining and machine learning techniques so as to obtain a real time burr detection model. This model could be implanted in the computer numerical control of the machine allowing the whole process to be automated and optimised. These techniques can be applied to other types of processes.