Metaheuristics are problem-solving methods which try to find near-optimal solutions to very hard ... more Metaheuristics are problem-solving methods which try to find near-optimal solutions to very hard optimization problems within an acceptable computational timeframe, where classical approaches usually fail, or cannot even been applied. Random mechanisms are an integral part of metaheuristics, given randomness has a role in dealing with algorithmic issues such as parameters tuning, adaptation, and combination of existing optimization techniques. In this paper, it is explored whether deterministic chaos can be suitably used instead of random processes within Variable Neighbourhood Search (VNS), a popular metaheuristic for combinatorial optimization. As a use case, in particular, the paper focuses on labelling graph problems, where VNS has been already used with success. These problems are formulated on an undirected labelled graph and consist on selecting the subset of labels such that the subgraph generated by these labels has, respectively, an optimal spanning tree or forest. The effects of using chaotic sequences in the VNS metaheuristic are investigated during several numerical tests. Different one-dimensional chaotic maps are applied to VNS in order to compare the performance of each map in finding the best solutions for this class of graph problems.
Immune computation is a relatively new field in computational intelligence when compared to artif... more Immune computation is a relatively new field in computational intelligence when compared to artificial neural networks, evolutionary computation and fuzzy systems. Inspired by the biological immune system, Immune Computation (also known as "Artificial Immune Systems") has been studied for many years and has attracted increasing interest among researchers. Ref. [1] by Farmer might be the first paper discussing the machine learning and adaptation principles in a biological immune system. The book edited by Dasgupta [2], which was published in 1999, is an earlier book focused on artificial immune systems. Nowadays, many models/algorithms of Artificial Immune Systems (AISs) have been proposed, which could be roughly classified into six categories: Negative Selection Algorithms [3], Immune Network Algorithms [4], Clonal Selection Algorithms [5], Dendritic Cell Algorithms [6], Negative Databases [7] and Negative Surveys [8]. Although there are many models/algorithms of AISs and they have been applied to a wide variety of applications, more efficient and practical artificial immune algorithms have been proposed in recent years [9, 10]. The application areas of AISs have also been extended. The aim of this special issue is precisely to present the state-of-the-art research developments on AISs. In this special issue, the accepted papers can be classified into three classes as follows:
The use of general descriptive names, registered names, trademarks, etc. in this publication does... more The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.
The ability to simulate gene expression and infer gene regulatory networks has vast potential app... more The ability to simulate gene expression and infer gene regulatory networks has vast potential applications in various fields, including medicine, agriculture, and environmental science. In recent years, machine learning approaches to simulate gene expression and infer gene regulatory networks have gained significant attention as a promising area of research. By simulating gene expression, we can gain insights into the complex mechanisms that control gene expression and how they are affected by various environmental factors. This knowledge can be used to develop new treatments for genetic diseases, improve crop yields, and better understand the evolution of species. In this article, we address this issue by focusing on a novel method capable of simulating the gene expression regulation of a group of genes and their mutual interactions. Our framework enables us to simulate the regulation of gene expression in response to alterations or perturbations that can affect the expression of a...
Community detection is a prominent research topic in Complex Network Analysis, and it constitutes... more Community detection is a prominent research topic in Complex Network Analysis, and it constitutes an important research field on all those areas where complex networks represent a powerful interpretation tool for describing and understanding systems involved in neuroscience, biology, social science, economy, and many others. A challenging approach to uncover the community structure in complex network, and then revealing the internal organization of nodes, is Modularity optimization. In this research paper, we present an immune optimization algorithm (opt-IA) developed to detect community structures, with the main aim to maximize the modularity produced by the discovered communities. In order to assess the performance of opt-IA, we compared it with an overall of 20 heuristics and metaheuristics, among which one Hyper-Heuristic method, using social and biological complex networks as data set. Unlike these algorithms, opt-IA is entirely based on a fully random search process, which in ...
Communications in Computer and Information Science, 2019
The use of general descriptive names, registered names, trademarks, service marks, etc. in this p... more The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Proceedings of the 9th International Joint Conference on Computational Intelligence, 2017
Interior lighting design is a challenging task where are involved multiple constraints that need ... more Interior lighting design is a challenging task where are involved multiple constraints that need to be optimized for producing an accurate illumination avoiding possible glare. This paper, then, takes up the issue of providing a computational tool able to produce a proper lighting plan in interior spaces for a comfortable and optimal vision in all environments, taking also into account the energy consumption as little as possible. For finding acceptable solutions we have used the metaphor of the genetic evolution in a multi-objective model, where individuals are lists of possible light sources, their positions and lighting levels. For finding acceptable solutions we have used the metaphor of the genetic evolution in a multi-objective model, where every individual is a list of light sources; their positions; and lighting levels. Further, for properly evaluating each individual, we have developed two conflicting objective functions, one for optimizing the level of brightness, and the second one for maximising the energy saving, satisfying, obviously, the additional constraints to respect the architectural structure to be lighted. From the randomly initial population of individuals generations are constructed using crossover and mutation operators, whilst the fittest offspring is preserved via an elitist Pareto-dominance selection approach. In addition to the multi-objective genetic algorithm, the 3D graphic software Blender has been used in order to reproduce the architectural space to be lighted, with the aim to evaluate then, the accuracy and uniformity of the produced lighting through a physical simulation of its brightness. The main goal of the developed tool is to provide to the designer (i.e. the decision maker) a set of interiors illumination design options, for the given environment to be lit, ensuring (i) uniform illumination distribution; (ii) accuracy of the illumination produced; (iii) avoiding harsh brightness, and glare; and (iv) low energy consumptions. Two case studies have been considered in our evaluation experiments, and for each of these the algorithm was performed on two different instances and with different types of complexity respectively.
Metaheuristics are problem-solving methods which try to find near-optimal solutions to very hard ... more Metaheuristics are problem-solving methods which try to find near-optimal solutions to very hard optimization problems within an acceptable computational timeframe, where classical approaches usually fail, or cannot even been applied. Random mechanisms are an integral part of metaheuristics, given randomness has a role in dealing with algorithmic issues such as parameters tuning, adaptation, and combination of existing optimization techniques. In this paper, it is explored whether deterministic chaos can be suitably used instead of random processes within Variable Neighbourhood Search (VNS), a popular metaheuristic for combinatorial optimization. As a use case, in particular, the paper focuses on labelling graph problems, where VNS has been already used with success. These problems are formulated on an undirected labelled graph and consist on selecting the subset of labels such that the subgraph generated by these labels has, respectively, an optimal spanning tree or forest. The effects of using chaotic sequences in the VNS metaheuristic are investigated during several numerical tests. Different one-dimensional chaotic maps are applied to VNS in order to compare the performance of each map in finding the best solutions for this class of graph problems.
Immune computation is a relatively new field in computational intelligence when compared to artif... more Immune computation is a relatively new field in computational intelligence when compared to artificial neural networks, evolutionary computation and fuzzy systems. Inspired by the biological immune system, Immune Computation (also known as "Artificial Immune Systems") has been studied for many years and has attracted increasing interest among researchers. Ref. [1] by Farmer might be the first paper discussing the machine learning and adaptation principles in a biological immune system. The book edited by Dasgupta [2], which was published in 1999, is an earlier book focused on artificial immune systems. Nowadays, many models/algorithms of Artificial Immune Systems (AISs) have been proposed, which could be roughly classified into six categories: Negative Selection Algorithms [3], Immune Network Algorithms [4], Clonal Selection Algorithms [5], Dendritic Cell Algorithms [6], Negative Databases [7] and Negative Surveys [8]. Although there are many models/algorithms of AISs and they have been applied to a wide variety of applications, more efficient and practical artificial immune algorithms have been proposed in recent years [9, 10]. The application areas of AISs have also been extended. The aim of this special issue is precisely to present the state-of-the-art research developments on AISs. In this special issue, the accepted papers can be classified into three classes as follows:
The use of general descriptive names, registered names, trademarks, etc. in this publication does... more The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.
The ability to simulate gene expression and infer gene regulatory networks has vast potential app... more The ability to simulate gene expression and infer gene regulatory networks has vast potential applications in various fields, including medicine, agriculture, and environmental science. In recent years, machine learning approaches to simulate gene expression and infer gene regulatory networks have gained significant attention as a promising area of research. By simulating gene expression, we can gain insights into the complex mechanisms that control gene expression and how they are affected by various environmental factors. This knowledge can be used to develop new treatments for genetic diseases, improve crop yields, and better understand the evolution of species. In this article, we address this issue by focusing on a novel method capable of simulating the gene expression regulation of a group of genes and their mutual interactions. Our framework enables us to simulate the regulation of gene expression in response to alterations or perturbations that can affect the expression of a...
Community detection is a prominent research topic in Complex Network Analysis, and it constitutes... more Community detection is a prominent research topic in Complex Network Analysis, and it constitutes an important research field on all those areas where complex networks represent a powerful interpretation tool for describing and understanding systems involved in neuroscience, biology, social science, economy, and many others. A challenging approach to uncover the community structure in complex network, and then revealing the internal organization of nodes, is Modularity optimization. In this research paper, we present an immune optimization algorithm (opt-IA) developed to detect community structures, with the main aim to maximize the modularity produced by the discovered communities. In order to assess the performance of opt-IA, we compared it with an overall of 20 heuristics and metaheuristics, among which one Hyper-Heuristic method, using social and biological complex networks as data set. Unlike these algorithms, opt-IA is entirely based on a fully random search process, which in ...
Communications in Computer and Information Science, 2019
The use of general descriptive names, registered names, trademarks, service marks, etc. in this p... more The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Proceedings of the 9th International Joint Conference on Computational Intelligence, 2017
Interior lighting design is a challenging task where are involved multiple constraints that need ... more Interior lighting design is a challenging task where are involved multiple constraints that need to be optimized for producing an accurate illumination avoiding possible glare. This paper, then, takes up the issue of providing a computational tool able to produce a proper lighting plan in interior spaces for a comfortable and optimal vision in all environments, taking also into account the energy consumption as little as possible. For finding acceptable solutions we have used the metaphor of the genetic evolution in a multi-objective model, where individuals are lists of possible light sources, their positions and lighting levels. For finding acceptable solutions we have used the metaphor of the genetic evolution in a multi-objective model, where every individual is a list of light sources; their positions; and lighting levels. Further, for properly evaluating each individual, we have developed two conflicting objective functions, one for optimizing the level of brightness, and the second one for maximising the energy saving, satisfying, obviously, the additional constraints to respect the architectural structure to be lighted. From the randomly initial population of individuals generations are constructed using crossover and mutation operators, whilst the fittest offspring is preserved via an elitist Pareto-dominance selection approach. In addition to the multi-objective genetic algorithm, the 3D graphic software Blender has been used in order to reproduce the architectural space to be lighted, with the aim to evaluate then, the accuracy and uniformity of the produced lighting through a physical simulation of its brightness. The main goal of the developed tool is to provide to the designer (i.e. the decision maker) a set of interiors illumination design options, for the given environment to be lit, ensuring (i) uniform illumination distribution; (ii) accuracy of the illumination produced; (iii) avoiding harsh brightness, and glare; and (iv) low energy consumptions. Two case studies have been considered in our evaluation experiments, and for each of these the algorithm was performed on two different instances and with different types of complexity respectively.
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