José Antonio Portilla-Figueras

A new grouping genetic algorithm for clustering problems

Highlights? A hybrid grouping-encoding algorithm for clustering problems is presented. ? Details on the encoding, operators and parallelization are given. ? Results in synthetic and real clustering problems are provided. In this paper we present a novel grouping genetic algorithm for clustering problems. Though there have been different approaches that have analyzed the performance of several genetic and evolutionary algorithms in clustering, the grouping-based approach has not been, to our knowledge, tested in this problem yet. In this paper we fully describe the grouping genetic algorithm for clustering, starting with the proposed encoding, different modifications of crossover and mutation operators, and also the description of a local search and an island model included in the algorithm, to improve the algorithms performance in the problem. We test the proposed grouping genetic algorithm in several experiments in synthetic and real data from public repositories, and compare its results with that of classical clustering approaches, such as K-means and DBSCAN algorithms, obtaining excellent results that confirm the goodness of the proposed grouping-based methodology.

Improving the training time of support vector regression algorithms through novel hyper-parameters search space reductions

The selection of hyper-parameters in support vector machines (SVM) is a key point in the training process of these models when applied to regression problems. Unfortunately, an exact method to obtain the optimal set of SVM hyper-parameters is unknown, and search algorithms are usually applied to obtain the best possible set of hyper-parameters. In general these search algorithms are implemented as grid searches, which are time consuming, so the computational cost of the SVM training process increases considerably. This paper presents a novel study of the effect of including reductions in the range of SVM hyper-parameters, in order to reduce the SVM training time, but with the minimum possible impact in its performance. The paper presents reduction in parameter C, by considering its relation with the rest of SVM hyper-parameters (@c and @e), through an approximation of the SVM model. On the other hand, we use some characteristics of the Gaussian kernel function and a previous result in the literature to obtain novel bounds for @c and @e hyper-parameters. The search space reductions proposed are evaluated in different regression problems from UCI and StatLib databases. All the experiments carried out applying the popular LIBSVM solver have shown that our approach reduces the SVM training time, maintaining the SVM performance similar to when the complete range in SVM parameters is considered.

Optimal switch location in mobile communication networks using hybrid genetic algorithms

The optimal positioning of switches in a mobile communication network is an important task, which can save costs and improve the performance of the network. In this paper we propose a model for establishing which are the best nodes of the network for allocating the available switches, and several hybrid genetic algorithms to solve the problem. The proposed model is based on the so-called capacitated p-median problem, which have been previously tackled in the literature. This problem can be split in two subproblems: the selection of the best set of switches, and a terminal assignment problem to evaluate each selection of switches. The hybrid genetic algorithms for solving the problem are formed by a conventional genetic algorithm, with a restricted search, and several local search heuristics. In this work we also develop novel heuristics for solving the terminal assignment problem in a fast and accurate way. Finally, we show that our novel approaches, hybridized with the genetic algorithm, outperform existing algorithms in the literature for the p-median problem.

A comparative study of two hybrid grouping evolutionary techniques for the capacitated P-median problem

This paper addresses the application of two different grouping-based algorithms to the so-called capacitated P-median problem (CPMP). The CPMP is an NP-complete problem, well-known in the operations research field, arising from a wide spectrum of applications in diverse fields such as telecommunications, manufacturing and industrial engineering. The CPMP problem has been previously tackled by using distinct algorithmic approaches, among which we focus on evolutionary computation techniques. The work presented herein elaborates on these evolutionary computation algorithms when applied to the CPMP, by evaluating the performance of a novel grouping genetic algorithm (GGA) and a novel grouping harmony search approach (GHS). Both GGA and GHS are hybridized with a specially tailored local search procedure for enhancing the overall performance of the algorithm in the particular CPMP scenario under consideration. This manuscript delves into the main characteristics of the proposed GGA and GHS schemes by thoroughly describing the grouping encoding procedure, the evolutionary operators (GGA) and the improvisation process (GHS), the aforementioned local search procedure and a repairing technique that accounts for the feasibility of the solutions iteratively provided by both algorithms. The performance of the proposed algorithms is compared with that of several existing evolutionary-based algorithms for CPMP instances of varying size, based on which it is concluded that GGA and GHS dominate any other approaches published so far in the literature, specially when the size of the CPMP increases. The experimental section of the paper tries to evaluate the goodness of the grouping encoding, and also the differences in behavior between the GGA and GHS due to the meta-heuristic algorithm used.

HYBRID PIFA-PATCH ANTENNA OPTIMIZED BY EVOLUTIONARY PROGRAMMING

In this paper we study the optimization process of a novel hybrid antenna, formed by a Planar Inverted-F Antenna (PIFA) and a coplanar patch in the same structure, and intended to be used in mobile communications and WIFI applications simultaneously. This hybrid device has been recently proposed and characterized in the literature, and it has been shown that it allows a bandwidth of 850MHz (49%) in the lower band and 630MHz (11.25%) in the upper band. In spite of these good performance results, the flne tuning of the joint PIFA-patch parameters in the hybrid antenna is a hard task, not easy to automatize. In this paper we propose the use of an Evolutionary Programming (EP) approach, an algorithm of the Evolutionary Computation family, which has been shown to be very efiective in continuous optimization problems. We use a real encoding of the antennas parameters and the CST Microwave Studio simulator to obtain the performance of the antenna. The simulator is therefore incorporated to the EP algorithm as a part of the antennas evaluation process. We will show that the EP is able to obtain very good sets of parameters in terms of the designer necessities, usually a larger bandwidth at the design frequencies. In this case, the bandwidth of

A novel grouping harmony search algorithm for the multiple-type access node location problem

In this paper we present a novel grouping harmony search algorithm for the Access Node Location Problem (ANLP) with different types of concentrators. The ANLP is a NP-hard problem where a set of distributed terminals, with distinct rate demands, must be assigned to a variable number of concentrators subject to capacity constraints. We consider the possibility of choosing between different concentrator models is given in order to provide service demand at different cost. The ANLP is relevant in communication networks design, and has been considered before within the design of MPLS networks, for example. The approach we propose to tackle the ANLP problem consists of a hybrid Grouping Harmony Search (GHS) algorithm with a local search method and a technique for repairing unfeasible solutions. Moreover, the presented scheme also includes the adaptation of the GHS to a differential scheme, where each proposed harmony is obtained from the same harmony in the previous iteration. This differential scheme is perfectly adapted to the specifications of the ANLP problem, as it utilizes the grouping concept based on the proximity between nodes, instead of being only based on the grouping concept. This allows for a higher efficiency on the searching process of the algorithm. Extensive Monte Carlo simulations in synthetic instances show that this proposal provides faster convergence rate, less computational complexity and better statistical performance than alternative algorithms for the ANLP, such as grouping genetic algorithms, specially when the size of the scenario increases. We also include practical results for the application of GHS to a real wireless network deployment problem in Bizkaia, northern Spain.

A decision support system for the automatic management of keep-clear signs based on support vector machines and geographic information systems

This paper presents a decision support system for automatic keep-clear signs management. The system consists of several modules. First of all, an acquisition module obtains images using a vehicle equipped with two recording cameras. A recognition module, which is based on Support Vector Machines (SVMs), analyzes each image and decides if there is a keep-clear sign in it. The images with keep-clear signs are included into a Geographical Information System (GIS) database. Finally in the management module, the data in the GIS are compared with the council database in order to decide actions such as repairing or reposition of signs, detection of possible frauds etc. We present the first tests of the system in a Spanish city (Meco, Madrid), where the systems is being tested for its application in the near future.

Teaching Advanced Features of Evolutionary Algorithms Using Japanese Puzzles

In this paper, a method to teach advanced features of evolutionary algorithms (EAs), using a famous game known as Japanese puzzles is presented. The authors show that Japanese puzzles are constrained combinatorial optimization problems, that can be solved using EAs with different encodings, and are challenging problems for EAs. Other features, such as special operators and local search heuristics and its hybridization with genetic algorithms, can also be taught using these puzzles. The authors report an experience using this method in a course taught at the Universidad de Alcalaacute, Madrid, Spain

Efficient citywide planning of open WiFi access networks using novel grouping harmony searchheuristics

This paper proposes the application of a novel meta-heuristic algorithm to the metropolitan wireless local area network deployment problem. In this problem, the coverage level of the deployed network must be maximized while meeting an assigned maximum budget, set beforehand. Specifically, we propose an approach based on the Harmony Search (HS) algorithm, with three main technical contributions: (1)the adaptation of the HS algorithm to a grouping scheme; (2)the adaptation of the improvisation operators driving the algorithm to the specific characteristics of the optimization problem to be tackled; and (3)its performance assessment via a simulated experiment inspired by real statistics in the city of Bilbao (Basque Country, northern Spain). Moreover, a comparison study of the proposed algorithm with a previous published grouping genetic algorithm is carried out, to further validate its performance. In light of the simulation results obtained from extensive experiments and several complexity considerations, we conclude that the proposed algorithm outperforms its genetically inspired counterpart, not only in terms of computation time, but also in the coverage level of the solution obtained.

A hybrid harmony search algorithm for the spread spectrum radar polyphase codes design problem

In this paper we present the application of a hybrid harmony search (HS) algorithm to the Spread-Spectrum Radar Polyphase (SSRP) codes design. Such a design can be formulated as a non-linear max-min optimization problem, hard to be solved using classical numerical techniques. Soft-computing approaches have then been successfully applied to solve the SSRP in the past, such as evolutionary computation techniques, variable neighborhood approaches or tabu search algorithms. In this paper we elaborate on the proposed hybrid HS approach, which consists of a naive implementation of the HS algorithm along with an adaptive-step gradient-guided local search procedure. Intensive computer simulations show that the proposed hybrid HS algorithm is able to outperform existing algorithms for the SSRP design problem (including the best reported so far), with significant differences in large-size SSRP instances.