Protein structure prediction in three dimensions represents a fundamental challenge in Structural Bioinformatics. Leveraging problem-specific information such as fragment insertion, secondary structure, and contact maps can significantly enhance the exploration of the search space. In this study, an evolutionary algorithm is introduced, which incorporates such problem information for protein structure prediction. The proposed method employs a dynamic speciation technique alongside fragment insertion to foster population diversity. To ensure a rich variety of fragments, a fragment library is constructed using the Rosetta Quota protocol. Additionally, information from contact maps and secondary structure is integrated into two selection strategies to facilitate a more thorough exploration of the conformational search space. The results of an experimental evaluation involving 9 proteins are presented, demonstrating competitive performance compared to existing literature. Evaluation metrics include RMSD, GDT, and processing time.

Abstract One of the most challenging problems in Bioinformatics is the finding of a protein conformation and it is known as the Protein Structure Prediction (PSP) problem. The main feature present in the AB off‐lattice model is the use of polarity as the main driving force to guide the optimization process. The present work proposes an adaptive evolutionary algorithm based on GPU that is applied to the 3D‐AB off‐lattice PSP problem. The proposed approach is named cuDSMjDE, and is composed of a Dynamic Speciation‐based Mutation Strategies Differential Evolution that employs the jDE mechanism to control F and CR parameters. A crowding strategy is also adopted, increasing competition between individuals and maintaining the diversity of solutions. All routines of the proposed algorithm are developed to run in GPU. During the experiments, eight Protein Data Bank sequences are employed. Results obtained concerning potential energy are competitive when compared with state‐of‐the‐art algorithms. The use of a massively parallel architecture promoted the necessary scalability with speedups up to 708.78 .

The protein structure prediction problem is one of the most important bioinformatics problems.Computational methods can be used to approach this problem and de novo methods are able to generate protein structures without the need of having known similar structures to the predicted protein.These methods transform the prediction problem into an optimization problem, using optimization models that combine different energy functions and high-level information.These models usually have only a single optimization objective.However, it is known that this single objective optimization approach may harm the optimization search due to the existence of conflicts between the different terms that compose the optimization objective.The proposed model has three objectives: energy function, secondary structure, and contact maps.A multi-objective Biased Random-Key Genetic Algorithm (BRKGA) with online parameter control, named MOBO, is proposed as the optimizer.The final predictor comprises two phases of the MOBO algorithm and selects a final structure using the MUFOLD-CL clustering method.Results obtained demonstrated that the proposed predictor generated highly competitive results with the literature.

Proteins are base molecules present in live organisms. The study of their structures and functions is of considerable importance for many application fields, particularly for the pharmaceutical area. This paper presents a multi-objective model to the Protein Structure Prediction problem, using three objectives: energy score, secondary structure information, and contact maps information. A BRKGA method is used as a global optimizer and adapted to work with multiple objective problems. Also, the MUFOLD-CL clustering method is applied to select a single predicted structure. Experiments were carried out to analyze the proposed model performance using state-of-the-art ab initio algorithms for comparison. Results obtained indicate that the proposed approach is competitive in terms of RMSD and GDT metrics.

The efficient management of resources after a disaster, must take place within a short time and efficiently. Therefore, resource scheduling protocol which shares resources among the victims, respecting time constraints, is decisive. In disaster scenarios, communication infrastructure is usually damaged and a commonly used solution for ensuring connectivity between victims and providers is Mobile Ad Hoc Network (MANET). Fire brigade and ambulances might be insufficient if the number of victims is high. Hence, in order to provide an efficient resource scheduling approach that cares about the energy consumption, the number of requesters attended and the processing time, we present a Scheduling Resource protocol. Thus, to reduce the provider dislocation time and increase the number of victims attended the protocol was modeled based on Genetic Algorithms. Results show that this approach maintains the trade-off between the number of victims attended, therefore, minimize the energy consumption.

Several works in GIScience propose approaches to identify general motion patterns through the analysis of objects’ trajectories. However, they are not suitable to identify functional relationships in scenarios where domain-dependent motion behaviors exist. In this work, we explore the identification of a particular pattern found in team-based invasion sports. We propose a method to identify defensive assignments between players of opposite teams based on the analysis of their trajectories. A defensive assignment happens when a defensive player blocks or hinders the progress of an opponent player inside his/her field. The defensive assignment can be classified as a behavioral pattern because it combines other behavioral patterns, such as pursuit, evasion, attack and defense. The identification of the assignments takes into account the following aspects of the players’ trajectories: proximity, position, speed and direction. The assessments pointed out that the method provides promising results, achieving 84% of success rate when compared with the analysis of a human specialist.

The success of cloud computing technology has leveraged the emergence of a large number of new companies providing cloud computing services. Choosing which cloud providers are the most suitable to attend consumers desired quality of service has become a hard problem. In order to qualify such providers, performance indicators (PIs) are useful tools for systematic and synthesized information collection. Thus, the problem approached in this work is to find the best set of cloud computing providers that satisfies a customer's request, with the least amount of providers and the lowest price. Hence, this work proposes a hybrid Genetic Algorithm (GA) to address this problem. In experiments, three approaches, using PIs as input, are employed: a simple matching algorithm, a GA and the proposed hybrid matching-GA approach. The hybrid method combines the qualities of both the matching algorithm and the GA showing promising results.

This paper presents an approach for resource scheduling in service discovery to MANETs operating in scenarios with time constraints, as emergency scenarios. The shared resources can be ambulances or rescue cars. With an efficient model of resource scaling, it is intended to provide the largest number of victims in the shortest time. In this work, we present an approach for resource scheduling modeled based on two concepts: Genetic Algorithm and A-Star Algorithm. The results obtained from the Network Simulator (NS3) show that the resource scheduling mechanism is efficient in response time and scalable in relation to different numbers of victims.

It is well known that face recognition (FR) systems cannot perform well under uncontrolled conditions, but there are no general and robust approaches with total immunity to all conditions. Hence, we present an adjustable FR framework with the aid of the Differential Evolution (DE) optimization algorithm. This approach implements several preprocessing and feature extraction techniques aiming to compensate the illumination variation. The main feature of the present work stands on the use of the DE which is responsible for choosing which strategies to use, as well as tunning the parameters involved. In this case study, we aim to address the illumination compensation problem applying on the well known Yale Extended B face dataset. According to the proposed FR framework, the DE can choose any combination of the following techniques and tune its necessary parameters achieving optimized values: the Gamma Intensity Correction (GIC), the Wavelet-based Illumination Normalization (WBIN), the Gaussian Blur, the Laplacian Edge Detection, the Discrete Wavelet Transform (DWT), the Discrete Cosine Transform (DCT), and the Local Binary Patterns (LBP). Our experimental analysis confirms that the proposed approach is suitable for FR using images under varying conditions. It is proved by the average recognition rate of 99.95% obtained using four different datasets.

Low-power and Lossy network (LNN) is commonly deployed in Internet of Things applications. It consists of a considerable amount of devices, also known as motes, with sensory capacity and wireless connectivity deployed in a wide geographical area. Such devices face limitations in terms of energy, memory and processing. A common topology in LNN is a mesh-like one. The communication between nodes often happens in a multi-hop fashion due to their limited transmission range. There is a need of efficient routing protocols, in terms of energy consumption. The present paper depicts a routing algorithm based on ant colony optimization. It also shows details about its implementation in the Contiki OS network system and analysis of its performance in comparison with the default Contiki’s mesh routing protocol. Results shown that the proposed algorithm had a better performance in terms of packet delivery rate, duty cycling and energy consumption.

In this work, a vision system has been developed using a frontal camera to monitor the driver, enabling to recognize the use of a cell phone while driving. It is estimated that 80% of car crashes and 65% of near collisions involved drivers who were inattentive in traffic for three seconds before the event. Five videos in real environments were generated to test the proposed system. The solution is a hybrid system and that uses a pattern recognition system (PR) for classification and a movement detection system (MD) for choosing the PR parameters at the end of each period of 3 seconds. The PR parameters are the threshold (frames identified as a cell phone use) and classifier selection. The classifiers are based on ANN, furthermore, the value of constants in neuron activation function and network training parameters were adopted with a genetic algorithm. Experimentally, it was established that when the movement indicates a possible use of the cell phone, the threshold 60% and an MLP/Gaussian classifier with seven neurons in intermediate layer are suitable; otherwise, a threshold of 85%, and MLP/Gaussian with two neurons in intermediate layer for classification are used. The average accuracy achieved was 91.68% in real environment scenes.

SUMMARY This paper reports the hybridization of the artificial bee colony (ABC) and a genetic algorithm (GA), in a hierarchical topology, a step ahead of a previous work. We used this parallel approach for solving the protein structure prediction problem using the three‐dimensional hydrophobic‐polar model with side‐chains (3DHP‐SC). The proposed method was run in a parallel processing environment (Beowulf cluster), and several aspects of the modeling and implementation are presented and discussed. The performance of the hybrid‐hierarchical ABC‐GA approach was compared with a hybrid‐hierarchical ABC‐only approach for four benchmark instances. Results show that the hybridization of the ABC with the GA improves the quality of solutions caused by the coevolution effect between them and their search behavior. Copyright © 2011 John Wiley & Sons, Ltd.

The paper proposes an algorithm for data mining called Ant-Miner (ant-colony-based data miner). The goal of Ant-Miner is to extract classification rules from data. The algorithm is inspired by both research on the behavior of real ant colonies and some data mining concepts as well as principles. We compare the performance of Ant-Miner with CN2, a well-known data mining algorithm for classification, in six public domain data sets. The results provide evidence that: 1) Ant-Miner is competitive with CN2 with respect to predictive accuracy, and 2) the rule lists discovered by Ant-Miner are considerably simpler (smaller) than those discovered by CN2.