12 results on '"Salcedo‐Sanz, Sancho"'
Search Results
2. Meta-Heuristic Algorithms for FPGA Segmented Channel Routing Problems with Non-standard Cost Functions
- Author
-
Salcedo-Sanz, Sancho, Xu, Yong, and Yao, Xin
- Published
- 2005
- Full Text
- View/download PDF
3. An efficient neuro-evolutionary hybrid modelling mechanism for the estimation of daily global solar radiation in the Sunshine State of Australia.
- Author
-
Salcedo-Sanz, Sancho, Deo, Ravinesh C., Cornejo-Bueno, Laura, Camacho-Gómez, Carlos, and Ghimire, Sujan
- Subjects
- *
SOLAR radiation , *CORAL reefs & islands , *GENETIC algorithms - Abstract
This research paper aims to develop a hybrid neuro-evolutionary wrapper-based model for daily global solar radiation estimation in the solar-rich Sunshine State of Queensland, Australia. To design a robust hybrid modelling mechanism, the Interim-ERA European Centre for Medium Range Weather Forecasting (ECMWF) Reanalysis data are employed to train and cross-validate the estimation model that is formulated by an evolutionary-type algorithm: the Coral Reefs Optimization (CRO) integrated with an Extreme Learning Machine (ELM) model. The hybrid CRO-(ELM) algorithm is applied in two stages: first for the feature selection process guided by an ELM algorithm (a class of fast training neural network tool) as the model’s fitness function to screen an optimal set of predictor variables and second, for the estimation of the solar radiation using the optimally screened variables by the final hybrid CRO-(ELM)-ELM system. To benchmark the performance of the hybrid CRO-ELM algorithm for this estimation problem we apply an alternative, yet a similar feature screening approach: the Grouping Genetic Algorithm encoded into the ELM-based model (GGA-(ELM) used as the predictor mechanism). After the feature selection process is performed by the CRO algorithm that extracts the data patterns for accurate estimation the alternative objective algorithm is applied (in this case the ELM again) to formulate the hybrid CRO-(ELM)-ELM modelling system. To provide a rigorous evaluation of the CRO-(ELM)-ELM hybrid system, alternative estimation approaches are considered: the Multivariate Adaptive Regression Splines (MARS), Multiple Linear Regression (MLR) and the Support Vector Regression (SVR). The hybrid CRO-(ELM)-ELM system is tested in a real problem where the results are evaluated by means of several statistical score metrics and diagnostic plots of the observed and the estimated daily global solar radiation in the testing phase. We show that the hybrid CRO-(ELM)-ELM model is able to yield promising results; thus improving those attained by the 7 alternative models (i.e., hybrid CRO-(ELM)-MARS, CRO-(ELM)-MLR and CRO-(ELM)-SVR and the GGA equivalent models). The study ascertains that the CRO-based hybrid system where a large pool of predictor data are carefully screened through a wrapper-based modelling system and the ELM model is applied as a objective estimation tool can be adopted as a qualified stratagem in solar radiation estimation problems. The proposed hybrid CRO-(ELM)-ELM system exhibits clear advantages over the alternative machine learning approaches tested and also over the other machine learning algorithms used without the feature selection tool; thus advocating its scientific utility in renewable energy applications. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
4. A Multi-Objective Genetic Algorithm for overlapping community detection based on edge encoding.
- Author
-
Bello-Orgaz, Gema, Salcedo-Sanz, Sancho, and Camacho, David
- Subjects
- *
SOCIAL network analysis , *GENETIC algorithms , *DATA mining , *GRAPH theory , *TOPOLOGY - Abstract
The Community Detection Problem (CDP) in Social Networks has been widely studied from different areas such as Data Mining, Graph Theory Physics, or Social Network Analysis, among others. This problem tries to divide a graph into different groups of nodes (communities), according to the graph topology. A partition is a division of the graph where each node belongs to only one community. However, a common feature observed in real-world networks is the existence of overlapping communities , where a given node can belong to more than one community. This paper presents a new Multi-Objective Genetic Algorithm (MOGA-OCD) designed to detect overlapping communities, by using measures related to the network connectivity. For this purpose, the proposed algorithm uses a phenotype-type encoding based on the edge information, and a new fitness function focused on optimizing two classical objectives in CDP: the first one is used to maximize the internal connectivity of the communities, whereas the second one is used to minimize the external connections to the rest of the graph. To select the most appropriate metrics for these objectives, a comparative assessment of several connectivity metrics has been carried out using real-world networks. Finally, the algorithm has been evaluated against other well-known algorithms from the state of the art in CDP. The experimental results show that the proposed approach improves overall the accuracy and quality of alternative methods in CDP, showing its effectiveness as a new powerful algorithm for detecting structured overlapping communities. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
5. A traffic-based evolutionary algorithm for network clustering.
- Author
-
Naldi, Maurizio, Salcedo-Sanz, Sancho, Carro-Calvo, Leopoldo, Laura, Luigi, Portilla-Figueras, Antonio, and Italiano, Giuseppe F.
- Subjects
EVOLUTIONARY algorithms ,GENETIC algorithms ,TOPOLOGY ,COMPUTER networks ,COMBINATORIAL optimization - Abstract
Highlights: [•] We propose a network clustering algorithm, based on the application of genetic operators and capable of exploiting the traffic information. [•] The algorithm can be successfully applied, rather than topology-based algorithms, to uncover relationships and detect communities whenever two nodes have a relationship though not being directly connected. [•] We compare our algorithm to other clustering algorithms, both evolutionary and non evolutionary. [•] Our algorithm outperforms all the other algorithms on all the real world datasets and outperforms all in the case of synthetic traffic matrices, excepting Newman's for the single case of a network of 75 nodes. [ABSTRACT FROM AUTHOR]
- Published
- 2013
- Full Text
- View/download PDF
6. A survey of repair methods used as constraint handling techniques in evolutionary algorithms.
- Author
-
Salcedo-Sanz, Sancho
- Subjects
GENETIC algorithms ,EVOLUTIONARY computation ,HEURISTIC programming ,ENCODING ,ARTIFICIAL neural networks ,SCIENTIFIC surveys ,PROBLEM solving ,COMPUTER engineering - Abstract
Abstract: This paper provides a survey of the most important repair heuristics used in evolutionary algorithms to solve constrained optimization problems. Popular techniques are reviewed, such as some crossover operators in permutation encoding, algorithms for fixing the number of 1s in binary encoded genetic algorithms, and more specialized techniques such as Hopfield neural networks, heuristics for graphs and trees, and repair heuristics in grouping genetic algorithms. The survey also gives some indications about the design and implementation of hybrid evolutionary algorithms, and provides a revision of the most important applications in which hybrid evolutionary techniques have been used. [Copyright &y& Elsevier]
- Published
- 2009
- Full Text
- View/download PDF
7. A hybrid grouping genetic algorithm for assigning students to preferred laboratory groups
- Author
-
Agustín-Blas, Luis E., Salcedo-Sanz, Sancho, Ortiz-García, Emilio G., Portilla-Figueras, Antonio, and Pérez-Bellido, Ángel M.
- Subjects
- *
COMBINATORIAL optimization , *GENETIC algorithms , *LABORATORIES , *CLASSES (Groups of students) , *GENETIC programming , *STUDENTS , *HIGHER education administration research - Abstract
Abstract: This paper presents a novel application of the hybrid grouping genetic algorithm in a problem related to university timetabling. Specifically, the assignment of students to laboratory groups is tackled. This problem includes an important constraint of capacity, due to laboratories usually have a maximum number of equips or computers available, so the number of total students in a group is constrained to be equal or less than the capacity of the laboratory. In addition, our approach considers the case in which the students provide a sorted list of preferred laboratory groups, so the objective of the assignment must take this point into account. A variation of the problem in which a balanced number of students per group is required (lecturer preferences) is also studied in this paper. The performance of the approach is shown in different test problems and in a real application in a Spanish University. [Copyright &y& Elsevier]
- Published
- 2009
- Full Text
- View/download PDF
8. Assignment of cells to switches in a cellular mobile network using a hybrid Hopfield network-genetic algorithm approach.
- Author
-
Salcedo-Sanz, Sancho and Yao, Xin
- Subjects
MOBILE communication systems ,COMBINATORIAL optimization ,GENETIC algorithms ,GENETIC programming - Abstract
Abstract: Handoff and cabling cost management plays a key role in the design of cellular telecommunications networks. The efficient assignment of cells to switches in this type of networks is an NP-complete problem which cannot be solved efficiently unless P=NP. This paper presents a hybrid Hopfield network-genetic algorithm approach to the cell-to-switches assignment problem, in which a Hopfield network manages the problem''s constraints, and a genetic algorithm searches for high quality solutions with the minimum possible cost in terms of handoff and cable displayed. We show, by means of computational experiments, the good performance of our approach to this problem. [Copyright &y& Elsevier]
- Published
- 2008
- Full Text
- View/download PDF
9. On the application of linear transformations for genetic algorithms optimization.
- Author
-
Salcedo-Sanz, Sancho and Bousoño-Calzón, Carlos
- Subjects
- *
GENETIC algorithms , *HEURISTIC , *MATHEMATICAL optimization , *MATHEMATICAL transformations , *ALGORITHMS - Abstract
This paper discusses new results about the effect of linear transformations (LTs) over the representation, the spectrum and the performance of standard genetic algorithms (GAs). Unitary LTs are used to set the basic outcomes and, although they can be used for theoretic GA-hard problems, their applicability is shown to be quite limited. Nevertheless, the redundancy of problems can be exploited by means of non-unitary LTs. We also propose an heuristic for construction of non-unitary LTs. Several experiments are performed in order to discuss the theoretical results of the paper. [ABSTRACT FROM AUTHOR]
- Published
- 2007
- Full Text
- View/download PDF
10. A Hybrid Hopfield Network-Genetic Algorithm Approach for the Terminal Assignment Problem.
- Author
-
Salcedo-sanz, Sancho and Xin Yao
- Subjects
- *
GENETIC programming , *GENETIC algorithms , *COMBINATORIAL optimization , *ARTIFICIAL neural networks , *TELECOMMUNICATION systems , *ARTIFICIAL intelligence - Abstract
This paper presents a hybrid Hopfield network-genetic algorithm (GA) approach to tackle the terminal assignment (TA) problem. TA involves determining minimum cost links to form a communications network, by connecting a given set of terminals to a given collection of concentrators. Some previous approaches provide very good results if the cost associated with assigning a single terminal to a given concentrator is known. However, there are situations in which the cost of a single assignment is not known in advance, and only the cost associated with feasible solutions can be calculated. In these situations, previous algorithms for TA based on greedy heuristics are no longer valid, or fail to get feasible solutions. Our approach involves a Hopfield neural network (HNN) which manages the problem's constraints, whereas a GA searches for high quality solutions with the minimum possible cost. We show that our algorithm is able to achieve feasible solutions to the TA in instances where the cost of a single assignment in not known in advance, improving the results obtained by previous approaches. We also show the applicability of our approach to other problems related to the TA. [ABSTRACT FROM AUTHOR]
- Published
- 2004
- Full Text
- View/download PDF
11. Enhancing Genetic Feature Selection Through Restricted Search and Walsh Analysis.
- Author
-
Salcedo-Sanz, Sancho, Camps-Valls, Gustavo, Pérez-Cruz, Fernando, Sepúlveda-Sanchis, José, and Bousoño-Calzón, Carlos
- Subjects
- *
GENETIC algorithms , *MACHINE learning , *COMBINATORIAL optimization , *ARTIFICIAL intelligence , *PERFORMANCE , *MATHEMATICAL optimization - Abstract
This article focuses on a twofold approach to improve the performance of genetic algorithms (GAs) in the feature selection problem (FSP). The feature selection problem (FSP) is an open issue in machine learning, which basically consists of finding a subset of input features that describes the underlying system structure as well or better than all available features. There is a more general problem, known as feature extraction problem, in which one has to construct a few features that encode all of the information contained in the available original ones; the FSP is, in fact a particularization of this problem.
- Published
- 2004
- Full Text
- View/download PDF
12. Offline Speaker Segmentation Using Genetic Algorithms and Mutual Information.
- Author
-
Salcedo-Sanz, Sancho, Gallardo-Antolín, Ascensión, Miguel Leiva-Murillo, José, and Bousoño-Calzón, Carlos
- Subjects
GENETIC algorithms ,ALGORITHMS ,COMBINATORIAL optimization ,GENETIC programming ,COMBINATORICS - Abstract
We present an evolutionary approach to speaker segmentation, an activity that is especially important prior to speaker recognition and audio content analysis tasks. Our approach consists of a genetic algorithm (GA), which encodes possible segmentations of an audio record, and a measure of mutual information between the audio data and possible segmentations, which is used as fitness function for the GA. We introduce a compact encoding of the problem into the GA which reduces the length of the GA individuals and improves the GA convergence properties. Our algorithm has been tested on the segmentation of real audio data, and its performance has been compared with several existing algorithms for speaker segmentation, obtaining very good results in all test problems. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
Catalog
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.