Modelo de computación evolutivo para redes sostenibles, eficientes y resistentes.

We present a new approach to adapt the differential evolution (DE) algorithm so that it can be applied in combinatorial optimization problems. The differential evolution algorithm has been proposed as an optimization algorithm for the continuous domain, using real numbers to encode the solutions, and its main operator, the mutation, uses a arithmetic operations to create a mutant using three different random solutions. This mutation operator cannot be used in combinatorial optimization problems, which have a domain of a discrete and finite set of objects. Based on this concept, we present an idea of representing each solution as a set, and replace the arithmetic operators in the classic DE genetic operators by set operators. Using a well known NP-hard problem, the traveling salesman problem (TSP), as an example of a combinatorial optimization problem, we study different possibilities for the mutation operator, presenting the advantages and disadvantages of each, before setting with the best one. We also explain the modifications made to adapt the algorithm for a multiobjective optimization algorithm. Some of these modifications are inherent to the different type of problems, other modification are proposed to improve the algorithm. Amongst the later modification are using more than one population in the evolution process. We also present a new self-adaptive variation of the multiobjective optimization algorithm, although this is not limited to the multi-objective case, and can be used also in the single-objective

Convex Hull-Based Multi-objective Genetic Programming for Maximizing ROC Performance

ROC is usually used to analyze the performance of classifiers in data mining. ROC convex hull (ROCCH) is the least convex major-ant (LCM) of the empirical ROC curve, and covers potential optima for the given set of classifiers. Generally, ROC performance maximization could be considered to maximize the ROCCH, which also means to maximize the true positive rate (tpr) and minimize the false positive rate (fpr) for each classifier in the ROC space. However, tpr and fpr are conflicting with each other in the ROCCH optimization process. Though ROCCH maximization problem seems like a multi-objective optimization problem (MOP), the special characters make it different from traditional MOP. In this work, we will discuss the difference between them and propose convex hull-based multi-objective genetic programming (CH-MOGP) to solve ROCCH maximization problems. Convex hull-based sort is an indicator based selection scheme that aims to maximize the area under convex hull, which serves as a unary indicator for the performance of a set of points. A selection procedure is described that can be efficiently implemented and follows similar design principles than classical hyper-volume based optimization algorithms. It is hypothesized that by using a tailored indicator-based selection scheme CH-MOGP gets more efficient for ROC convex hull approximation than algorithms which compute all Pareto optimal points. To test our hypothesis we compare the new CH-MOGP to MOGP with classical selection schemes, including NSGA-II, MOEA/D) and SMS-EMOA. Meanwhile, CH-MOGP is also compared with traditional machine learning algorithms such as C4.5, Naive Bayes and Prie. Experimental results based on 22 well-known UCI data sets show that CH-MOGP outperforms significantly traditional EMOAs

(Global) Optimization: Historical notes and recent developments

Recent developments in (Global) Optimization are surveyed in this paper. We collected and commented quite a large number of recent references which, in our opinion, well represent the vivacity, deepness, and width of scope of current computational approaches and theoretical results about nonconvex optimization problems. Before the presentation of the recent developments, which are subdivided into two parts related to heuristic and exact approaches, respectively, we briefly sketch the origin of the discipline and observe what, from the initial attempts, survived, what was not considered at all as well as a few approaches which have been recently rediscovered, mostly in connection with machine learning

Privacy-Preserving Decentralized Optimization and Event Localization

This dissertation considers decentralized optimization and its applications. On the one hand, we address privacy preservation for decentralized optimization, where N agents cooperatively minimize the sum of N convex functions private to these individual agents. In most existing decentralized optimization approaches, participating agents exchange and disclose states explicitly, which may not be desirable when the states contain sensitive information of individual agents. The problem is more acute when adversaries exist which try to steal information from other participating agents. To address this issue, we first propose two privacy-preserving decentralized optimization approaches based on ADMM (alternating direction method of multipliers) and subgradient method, respectively, by leveraging partially homomorphic cryptography. To our knowledge, this is the first time that cryptographic techniques are incorporated in a fully decentralized setting to enable privacy preservation in decentralized optimization in the absence of any third party or aggregator. To facilitate the incorporation of encryption in a fully decentralized manner, we also introduce a new ADMM which allows time-varying penalty matrices and rigorously prove that it has a convergence rate of O(1/t). However, given that encryption-based algorithms unavoidably bring about extra computational and communication overhead in real-time optimization [61], we then propose another novel privacy solution for decentralized optimization based on function decomposition and ADMM which enables privacy without incurring large communication/computational overhead. On the other hand, we address the application of decentralized optimization to the event localization problem, which plays a fundamental role in many wireless sensor network applications such as environmental monitoring, homeland security, medical treatment, and health care. The event localization problem is essentially a non-convex and non-smooth problem. We address such a problem in two ways. First, a completely decentralized solution based on augmented Lagrangian methods and ADMM is proposed to solve the non-smooth and non-convex problem directly, rather than using conventional convex relaxation techniques. However, this algorithm requires the target event to be within the convex hull of the deployed sensors. To address this issue, we propose another two scalable distributed algorithms based on ADMM and convex relaxation, which do not require the target event to be within the convex hull of the deployed sensors. Simulation results confirm effectiveness of the proposed algorithms

Multiobjective optimization of classifiers by means of 3-D convex Hull based evolutionary algorithms

The receiver operating characteristic (ROC) and detection error tradeoff (DET) curves are frequently used in the machine learning community to analyze the performance of binary classifiers. Recently, the convex-hull-based multiobjective genetic programming algorithm was proposed and successfully applied to maximize the convex hull area for binary classification problems by minimizing false positive rate and maximizing true positive rate at the same time using indicator-based evolutionary algorithms. The area under the ROC curve was used for the performance assessment and to guide the search. Here we extend this research and propose two major advancements: Firstly we formulate the algorithm in detection error tradeoff space, minimizing false positives and false negatives, with the advantage that misclassification cost tradeoff can be assessed directly. Secondly, we add complexity as an objective function, which gives rise to a 3D objective space (as opposed to a 2D previous ROC space). A domain specific performance indicator for 3D Pareto front approximations, the volume above DET surface, is introduced, and used to guide the indicator -based evolutionary algorithm to find optimal approximation sets. We assess the performance of the new algorithm on designed theoretical problems with different geometries of Pareto fronts and DET surfaces, and two application-oriented benchmarks: (1) Designing spam filters with low numbers of false rejects, false accepts, and low computational cost using rule ensembles, and (2) finding sparse neural networks for binary classification of test data from the UCI machine learning benchmark. The results show a high performance of the new algorithm as compared to conventional methods for multicriteria optimization.info:eu-repo/semantics/submittedVersio

Early Structural Assessment and Optimisation of Passenger Ships

peer reviewedA multi-criteria optimisation of a passenger ship is conducted in this paper. Minimum production cost and minimum steel weight are the both objective studied. Moreover the study answers to the following question: "From when will the higher costs of high tensile steel should be offset by a gain of steel weight?". For a passenger ship, a significant reduction of the steel weight, for a controlled raise of the gravity centre, should lead either to a reduction of fuel consumption either to an additional deck, which for a ship owner means a faster return on investment. Pareto frontiers are obtained and results are validated by classification rules

Motion Planning

Motion planning is a fundamental function in robotics and numerous intelligent machines. The global concept of planning involves multiple capabilities, such as path generation, dynamic planning, optimization, tracking, and control. This book has organized different planning topics into three general perspectives that are classified by the type of robotic applications. The chapters are a selection of recent developments in a) planning and tracking methods for unmanned aerial vehicles, b) heuristically based methods for navigation planning and routes optimization, and c) control techniques developed for path planning of autonomous wheeled platforms

Taking aim at moving targets in computational cell migration

Cell migration is central to the development and maintenance of multicellular organisms. Fundamental understanding of cell migration can, for example, direct novel therapeutic strategies to control invasive tumor cells. However, the study of cell migration yields an overabundance of experimental data that require demanding processing and analysis for results extraction. Computational methods and tools have therefore become essential in the quantification and modeling of cell migration data. We review computational approaches for the key tasks in the quantification of in vitro cell migration: image pre-processing, motion estimation and feature extraction. Moreover, we summarize the current state-of-the-art for in silico modeling of cell migration. Finally, we provide a list of available software tools for cell migration to assist researchers in choosing the most appropriate solution for their needs