Artificial Immune Systems - Models, algorithms and applications

Copyright © 2010 Academic Research Publishing Agency.This article has been made available through the Brunel Open Access Publishing Fund.Artificial Immune Systems (AIS) are computational paradigms that belong to the computational intelligence family and are inspired by the biological immune system. During the past decade, they have attracted a lot of interest from researchers aiming to develop immune-based models and techniques to solve complex computational or engineering problems. This work presents a survey of existing AIS models and algorithms with a focus on the last five years.This article is available through the Brunel Open Access Publishing Fun

Analyzing the solutions of DEA through information visualization and data mining techniques: SmartDEA framework

Data envelopment analysis (DEA) has proven to be a useful tool for assessing efficiency or productivity of organizations, which is of vital practical importance in managerial decision making. DEA provides a significant amount of information from which analysts and managers derive insights and guidelines to promote their existing performances. Regarding to this fact, effective and methodologic analysis and interpretation of DEA solutions are very critical. The main objective of this study is then to develop a general decision support system (DSS) framework to analyze the solutions of basic DEA models. The paper formally shows how the solutions of DEA models should be structured so that these solutions can be examined and interpreted by analysts through information visualization and data mining techniques effectively. An innovative and convenient DEA solver, SmartDEA, is designed and developed in accordance with the proposed analysis framework. The developed software provides a DEA solution which is consistent with the framework and is ready-to-analyze with data mining tools, through a table-based structure. The developed framework is tested and applied in a real world project for benchmarking the vendors of a leading Turkish automotive company. The results show the effectiveness and the efficacy of the proposed framework

Hybridization of Evolutionary Algorithms

Evolutionary algorithms are good general problem solver but suffer from a lack of domain specific knowledge. However, the problem specific knowledge can be added to evolutionary algorithms by hybridizing. Interestingly, all the elements of the evolutionary algorithms can be hybridized. In this chapter, the hybridization of the three elements of the evolutionary algorithms is discussed: the objective function, the survivor selection operator and the parameter settings. As an objective function, the existing heuristic function that construct the solution of the problem in traditional way is used. However, this function is embedded into the evolutionary algorithm that serves as a generator of new solutions. In addition, the objective function is improved by local search heuristics. The new neutral selection operator has been developed that is capable to deal with neutral solutions, i.e. solutions that have the different representation but expose the equal values of objective function. The aim of this operator is to directs the evolutionary search into a new undiscovered regions of the search space. To avoid of wrong setting of parameters that control the behavior of the evolutionary algorithm, the self-adaptation is used. Finally, such hybrid self-adaptive evolutionary algorithm is applied to the two real-world NP-hard problems: the graph 3-coloring and the optimization of markers in the clothing industry. Extensive experiments shown that these hybridization improves the results of the evolutionary algorithms a lot. Furthermore, the impact of the particular hybridizations is analyzed in details as well

Channel Selection in Uncoordinated IEEE 802.11 Networks Using Graph Coloring

[EN] One of the big challenges in decentralized Wi-Fi networks is how to select channels for the different access points (APs) and their associated stations (STAs) in order to minimize interference and hence maximize throughput. Interestingly enough, de facto standards in terms of uncoordinated channel selection are quite simple, and in many cases result in fairly suboptimal channel allocations. Here, we explore how graph coloring can be used to evaluate and inform decisions on Wi-Fi channel selection in uncoordinated settings. Graph coloring, in its most basic form, is a classic mathematical problem where colors have to be assigned to nodes in a graph while avoiding assigning the same color to adjacent nodes. In this paper, we modeled Wi-Fi uncoordinated channel selection as a graph coloring problem and evaluated the performance of different uncoordinated channel selection techniques in a set of representative scenarios of residential buildings. The results confirm some of the widely accepted consensus regarding uncoordinated channel selection but also provide some new insights. For instance, in some settings, it would be better to delegate the decision on which channel to use to transmit the STAs, rather than having the AP make the decision on its own, which is the usual way.This publication is part of Project TED2021-131387B-I00 funded by MCIN/AEI/10.13039/501100011033 and by the European Union "NextGenerationEU"/PRTR and of Project PID2021-123168NB-I00 funded by MCIN/AEI/10.13039/501100011033/FEDER, UE. Ivan Marsa-Maestre and David Orden are partially funded by Project SB-PLY/19/180501/000171 of the Junta de Comunidades de Castilla-La Mancha and FEDER and by Project WiDAI (CM/JIN/2021-004) of the Comunidad de Madrid and University of Alcala. Jose Manuel Gimenez-Guzman, Ivan Marsa-Maestre, and David Herranz-Oliveros are also funded by Project PID2019-104855RB-I00/AEI/10.13039/501100011033 of the Spanish Ministry of Science and Innovation. David Orden is also partially supported by Project PID2019-104129GB-I00/AEI/10.13039/501100011033 of the Spanish Ministry of Science and Innovation. The APC was funded by Project TED2021-131387B-I00 funded by MCIN/AEI/10.13039/501100011033 and by the European Union "NextGenerationEU"/PRTR.Gimenez-Guzman, JM.; Marsá-Maestre, I.; De La Hoz, E.; Orden, D.; Herranz-Oliveros, D. (2023). Channel Selection in Uncoordinated IEEE 802.11 Networks Using Graph Coloring. Sensors. 23(13). https://doi.org/10.3390/s23135932231

Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition

In this work we present a framework for the recognition of natural scene text. Our framework does not require any human-labelled data, and performs word recognition on the whole image holistically, departing from the character based recognition systems of the past. The deep neural network models at the centre of this framework are trained solely on data produced by a synthetic text generation engine -- synthetic data that is highly realistic and sufficient to replace real data, giving us infinite amounts of training data. This excess of data exposes new possibilities for word recognition models, and here we consider three models, each one "reading" words in a different way: via 90k-way dictionary encoding, character sequence encoding, and bag-of-N-grams encoding. In the scenarios of language based and completely unconstrained text recognition we greatly improve upon state-of-the-art performance on standard datasets, using our fast, simple machinery and requiring zero data-acquisition costs

Genetic algorithms

Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solving methods) based loosely on the processes of natural genetics and Darwinian survival of the fittest. Basic genetic algorithms concepts are introduced, genetic algorithm applications are introduced, and results are presented from a project to develop a software tool that will enable the widespread use of genetic algorithm technology

ISBIS 2016: Meeting on Statistics in Business and Industry

This Book includes the abstracts of the talks presented at the 2016 International Symposium on Business and Industrial Statistics, held at Barcelona, June 8-10, 2016, hosted at the Universitat Politècnica de Catalunya - Barcelona TECH, by the Department of Statistics and Operations Research. The location of the meeting was at ETSEIB Building (Escola Tecnica Superior d'Enginyeria Industrial) at Avda Diagonal 647. The meeting organizers celebrated the continued success of ISBIS and ENBIS society, and the meeting draw together the international community of statisticians, both academics and industry professionals, who share the goal of making statistics the foundation for decision making in business and related applications. The Scientific Program Committee was constituted by: David Banks, Duke University Amílcar Oliveira, DCeT - Universidade Aberta and CEAUL Teresa A. Oliveira, DCeT - Universidade Aberta and CEAUL Nalini Ravishankar, University of Connecticut Xavier Tort Martorell, Universitat Politécnica de Catalunya, Barcelona TECH Martina Vandebroek, KU Leuven Vincenzo Esposito Vinzi, ESSEC Business Schoo