Abstract Models of linear programming problems with fuzzy constraints are very well known in the ... more Abstract Models of linear programming problems with fuzzy constraints are very well known in the current literature. In almost all cases, to solve these problems, linear membership functions are used because they have very good properties and are very easy to manipulate. In some cases, however, because of the knowledge that the decision maker has, such membership functions could be modeled as nonlinear, although the complexity of the problem could increase.
Resumen Los Algoritmos Gen eticos son procedimientos adaptativos para la b usqueda de soluciones ... more Resumen Los Algoritmos Gen eticos son procedimientos adaptativos para la b usqueda de soluciones en espacios complejos inspirados en la evoluci on biol ogica, con patrones de operaciones basados en el principio darwiniano de reproducci on y supervivencia de los individuos que mejor se adaptan al entorno en el que viven.
Abstract Memetic algorithms with continuous local search methods have arisen as effective tools t... more Abstract Memetic algorithms with continuous local search methods have arisen as effective tools to address the difficulty of obtaining reliable solutions of high precision for complex continuous optimisation problems. There exists a group of continuous local search algorithms that stand out as exceptional local search optimisers. However, on some occasions, they may become very expensive, because of the way they exploit local information to guide the search process.
Abstract—In this paper we present a new approach for laserbased environment device control system... more Abstract—In this paper we present a new approach for laserbased environment device control systems by laser pointer for handicapped people. The paper proposes the design of a Fuzzy Rule Base System for laser pointer detection. The idea is to improve the success rate of the previous approaches decreasing as much as possible the false offs, ie, the detection of a false laser spot (since this could lead to dangerous situations).
In complex multidimensional problems with a highly nonlinear input-output relation, inconsistent ... more In complex multidimensional problems with a highly nonlinear input-output relation, inconsistent or redundant rules can be found in the fuzzy model rule base, which can result in a loss of accuracy and interpretability. Moreover, the rules could not cooperate in the best possible way. It is known that the use of rule weights as a local tuning of linguistic rules, enables the linguistic fuzzy models to cope with inefficient and/or redundant rules and thereby enhances the robustness, flexibility and system modeling capability.
Abstract The interpretability of the results obtained and the quality measures used both to extra... more Abstract The interpretability of the results obtained and the quality measures used both to extract and evaluate the rules are two key aspects of subgroup discovery. In this study, we analyse the influence of the type of rule used to extract knowledge in subgroup discovery, and the quality measures more adapted to the evolutionary algorithms for subgroup discovery developed so far. The adaptation of the NMEF-SD algorithm to extract disjunctive formal norm rules is also presented.
Summary. Preference relations are the most common representation structures of information used i... more Summary. Preference relations are the most common representation structures of information used in decision making problems because they are useful tool in modelling decision processes, above all when we want to aggregate experts' preferences into group preferences. Therefore, to establish rationality properties to be verified by preference relations is very important in the design of good decision making models.
Class imbalance is among the most persistent complications which may confront the traditional sup... more Class imbalance is among the most persistent complications which may confront the traditional supervised learning task in real-world applications. The problem occurs, in the binary case, when the number of instances in one class significantly outnumbers the number of instances in the other class. This situation is a handicap when trying to identify the minority class, as the learning algorithms are not usually adapted to such characteristics.
Abstract. Unbalanced data in a classification problem appears when there are many more instances ... more Abstract. Unbalanced data in a classification problem appears when there are many more instances of some classes than others. Several solutions were proposed to solve this problem at data level by undersampling. The aim of this work is to propose evolutionary prototype selection algorithms that tackle the problem of unbalanced data by using a new fitness function.
The aggregation of preference relations in group decision-making (GDM) problems can be carried ou... more The aggregation of preference relations in group decision-making (GDM) problems can be carried out based on either the reliability of the preference values to be aggregated, as is the case with ordered weighted averaging operators, or on the reliability of the source of information that provided the preferences, as is the case with weighted mean operators.
Abstract Prototype selection is a research field which has been active for more than four decades... more Abstract Prototype selection is a research field which has been active for more than four decades. As a result, a great number of methods tackling the prototype selection problem have been proposed in the literature. This technical report provides a survey of the most representative algorithms developed so far.
The difficulty to solve multiple objective combinatorial optimization problems with traditional t... more The difficulty to solve multiple objective combinatorial optimization problems with traditional techniques has urged researchers to look for alternative, better performing approaches for them. Recently, several algorithms have been proposed which are based on the Ant Colony Optimization metaheuristic.
In this paper, we propose a replacement strategy for steady-state genetic algorithms that conside... more In this paper, we propose a replacement strategy for steady-state genetic algorithms that considers two features of the candidate chromosome to be included into the population: a measure of the contribution of diversity to the population and the fitness function. In particular, the proposal tries to replace an individual in the population with worse values for these two features.
Summary. Subgroup discovery can be defined as a form of supervised inductive learning in which, g... more Summary. Subgroup discovery can be defined as a form of supervised inductive learning in which, given a population of individuals and a specific property of individuals in which we are interested, find population subgroups that have the most unusual distributional characteristics with respect to the property of interest.
Abstract In those problems that deal with multiple sources of linguistic information we can find ... more Abstract In those problems that deal with multiple sources of linguistic information we can find problems defined in contexts where the linguistic assessments are assessed in linguistic term sets with different granularity of uncertainty and/or semantics (multigranular linguistic contexts). Different approaches have been developed to manage this type of contexts, that unify the multigranular linguistic information in an unique linguistic term set for an easy management of the information.
In this paper, we present a multistage genetic learning process for obtaining linguistic fuzzy ru... more In this paper, we present a multistage genetic learning process for obtaining linguistic fuzzy rule-based classification systems that integrates fuzzy reasoning methods cooperating with the fuzzy rule base and learns the best set of linguistic hedges for the linguistic variable terms. We show the application of the genetic learning process to two well known sample bases, and compare the results with those obtained from different learning algorithms.
Abstract A major problem in the use of genetic algorithms is premature convergence. One approach ... more Abstract A major problem in the use of genetic algorithms is premature convergence. One approach for dealing with this problem is the distributed genetic algorithm model. Its basic idea is to keep, in parallel, several subpopulations that are processed by genetic algorithms, with each one being independent of the others. Making distinctions between the subpopulations by applying genetic algorithms with different configurations, we obtain the so-railed heterogeneous distributed genetic algorithms.
Abstract Models of linear programming problems with fuzzy constraints are very well known in the ... more Abstract Models of linear programming problems with fuzzy constraints are very well known in the current literature. In almost all cases, to solve these problems, linear membership functions are used because they have very good properties and are very easy to manipulate. In some cases, however, because of the knowledge that the decision maker has, such membership functions could be modeled as nonlinear, although the complexity of the problem could increase.
Resumen Los Algoritmos Gen eticos son procedimientos adaptativos para la b usqueda de soluciones ... more Resumen Los Algoritmos Gen eticos son procedimientos adaptativos para la b usqueda de soluciones en espacios complejos inspirados en la evoluci on biol ogica, con patrones de operaciones basados en el principio darwiniano de reproducci on y supervivencia de los individuos que mejor se adaptan al entorno en el que viven.
Abstract Memetic algorithms with continuous local search methods have arisen as effective tools t... more Abstract Memetic algorithms with continuous local search methods have arisen as effective tools to address the difficulty of obtaining reliable solutions of high precision for complex continuous optimisation problems. There exists a group of continuous local search algorithms that stand out as exceptional local search optimisers. However, on some occasions, they may become very expensive, because of the way they exploit local information to guide the search process.
Abstract—In this paper we present a new approach for laserbased environment device control system... more Abstract—In this paper we present a new approach for laserbased environment device control systems by laser pointer for handicapped people. The paper proposes the design of a Fuzzy Rule Base System for laser pointer detection. The idea is to improve the success rate of the previous approaches decreasing as much as possible the false offs, ie, the detection of a false laser spot (since this could lead to dangerous situations).
In complex multidimensional problems with a highly nonlinear input-output relation, inconsistent ... more In complex multidimensional problems with a highly nonlinear input-output relation, inconsistent or redundant rules can be found in the fuzzy model rule base, which can result in a loss of accuracy and interpretability. Moreover, the rules could not cooperate in the best possible way. It is known that the use of rule weights as a local tuning of linguistic rules, enables the linguistic fuzzy models to cope with inefficient and/or redundant rules and thereby enhances the robustness, flexibility and system modeling capability.
Abstract The interpretability of the results obtained and the quality measures used both to extra... more Abstract The interpretability of the results obtained and the quality measures used both to extract and evaluate the rules are two key aspects of subgroup discovery. In this study, we analyse the influence of the type of rule used to extract knowledge in subgroup discovery, and the quality measures more adapted to the evolutionary algorithms for subgroup discovery developed so far. The adaptation of the NMEF-SD algorithm to extract disjunctive formal norm rules is also presented.
Summary. Preference relations are the most common representation structures of information used i... more Summary. Preference relations are the most common representation structures of information used in decision making problems because they are useful tool in modelling decision processes, above all when we want to aggregate experts' preferences into group preferences. Therefore, to establish rationality properties to be verified by preference relations is very important in the design of good decision making models.
Class imbalance is among the most persistent complications which may confront the traditional sup... more Class imbalance is among the most persistent complications which may confront the traditional supervised learning task in real-world applications. The problem occurs, in the binary case, when the number of instances in one class significantly outnumbers the number of instances in the other class. This situation is a handicap when trying to identify the minority class, as the learning algorithms are not usually adapted to such characteristics.
Abstract. Unbalanced data in a classification problem appears when there are many more instances ... more Abstract. Unbalanced data in a classification problem appears when there are many more instances of some classes than others. Several solutions were proposed to solve this problem at data level by undersampling. The aim of this work is to propose evolutionary prototype selection algorithms that tackle the problem of unbalanced data by using a new fitness function.
The aggregation of preference relations in group decision-making (GDM) problems can be carried ou... more The aggregation of preference relations in group decision-making (GDM) problems can be carried out based on either the reliability of the preference values to be aggregated, as is the case with ordered weighted averaging operators, or on the reliability of the source of information that provided the preferences, as is the case with weighted mean operators.
Abstract Prototype selection is a research field which has been active for more than four decades... more Abstract Prototype selection is a research field which has been active for more than four decades. As a result, a great number of methods tackling the prototype selection problem have been proposed in the literature. This technical report provides a survey of the most representative algorithms developed so far.
The difficulty to solve multiple objective combinatorial optimization problems with traditional t... more The difficulty to solve multiple objective combinatorial optimization problems with traditional techniques has urged researchers to look for alternative, better performing approaches for them. Recently, several algorithms have been proposed which are based on the Ant Colony Optimization metaheuristic.
In this paper, we propose a replacement strategy for steady-state genetic algorithms that conside... more In this paper, we propose a replacement strategy for steady-state genetic algorithms that considers two features of the candidate chromosome to be included into the population: a measure of the contribution of diversity to the population and the fitness function. In particular, the proposal tries to replace an individual in the population with worse values for these two features.
Summary. Subgroup discovery can be defined as a form of supervised inductive learning in which, g... more Summary. Subgroup discovery can be defined as a form of supervised inductive learning in which, given a population of individuals and a specific property of individuals in which we are interested, find population subgroups that have the most unusual distributional characteristics with respect to the property of interest.
Abstract In those problems that deal with multiple sources of linguistic information we can find ... more Abstract In those problems that deal with multiple sources of linguistic information we can find problems defined in contexts where the linguistic assessments are assessed in linguistic term sets with different granularity of uncertainty and/or semantics (multigranular linguistic contexts). Different approaches have been developed to manage this type of contexts, that unify the multigranular linguistic information in an unique linguistic term set for an easy management of the information.
In this paper, we present a multistage genetic learning process for obtaining linguistic fuzzy ru... more In this paper, we present a multistage genetic learning process for obtaining linguistic fuzzy rule-based classification systems that integrates fuzzy reasoning methods cooperating with the fuzzy rule base and learns the best set of linguistic hedges for the linguistic variable terms. We show the application of the genetic learning process to two well known sample bases, and compare the results with those obtained from different learning algorithms.
Abstract A major problem in the use of genetic algorithms is premature convergence. One approach ... more Abstract A major problem in the use of genetic algorithms is premature convergence. One approach for dealing with this problem is the distributed genetic algorithm model. Its basic idea is to keep, in parallel, several subpopulations that are processed by genetic algorithms, with each one being independent of the others. Making distinctions between the subpopulations by applying genetic algorithms with different configurations, we obtain the so-railed heterogeneous distributed genetic algorithms.
—This paper proposes a multilabel fuzzy decision tree classifier named FuzzDTML. The algorithm us... more —This paper proposes a multilabel fuzzy decision tree classifier named FuzzDTML. The algorithm uses generalized fuzzy entropy, aggregated over all labels, to choose the best attribute for growing the tree. The proposed algorithm also can generate leaves predicting partial label sets, which can incorporate to some degree the dependence among labels, as well as produce more interpretable models. An empirical analysis shows that, although the algorithm does not yet incorporate pruning nor fuzzy interval adjustment phases, it is competitive with other tree based approaches for multilabel classification, with better performance in data sets having numerical features that can be fuzzified.
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