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Tackling realcoded genetic algorithms: operators and tools for the behavioural analysis
 Arti Intelligence Reviews
, 1998
"... Abstract. Genetic algorithms play a significant role, as search techniques for handling complex spaces, in many fields such as artificial intelligence, engineering, robotic, etc. Genetic algorithms are based on the underlying genetic process in biological organisms and on the natural evolution prin ..."
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Cited by 142 (25 self)
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Abstract. Genetic algorithms play a significant role, as search techniques for handling complex spaces, in many fields such as artificial intelligence, engineering, robotic, etc. Genetic algorithms are based on the underlying genetic process in biological organisms and on the natural evolution principles of populations. These algorithms process a population of chromosomes, which represent search space solutions, with three operations: selection, crossover and mutation. Under its initial formulation, the search space solutions are coded using the binary alphabet. However, the good properties related with these algorithms do not stem from the use of this alphabet; other coding types have been considered for the representation issue, such as real coding, which would seem particularly natural when tackling optimization problems of parameters with variables in continuous domains. In this paper we review the features of realcoded genetic algorithms. Different models of genetic operators and some mechanisms available for studying the behaviour of this type of genetic algorithms are revised and compared. Key words: genetic algorithms, real coding, continuous search spaces Abbreviations: GAs – genetic algorithms; BCGA – binarycoded genetic algorithm; RCGA – realcoded genetic algorithm
Fuzzy Queries in Multimedia Database Systems
, 1998
"... There are essential differences between multimedia databases (which may contain complicated objects, such as images), and traditional databases. These differences lead to interesting new issues, and in particular cause us to consider new types of queries. For example, in a multimedia database it is ..."
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Cited by 127 (1 self)
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There are essential differences between multimedia databases (which may contain complicated objects, such as images), and traditional databases. These differences lead to interesting new issues, and in particular cause us to consider new types of queries. For example, in a multimedia database it is reasonable and natural to ask for images that are somehow "similar to" some fixed image. Furthermore, there are different ways of obtaining and accessing information in a multimedia database than information in a traditional database. For example, in a multimedia database, it might be reasonable to have a query that asks for, say, the top 10 images that are similar to a fixed image. This is in contrast to a relational database, where the answer to a query is simply a set. (Of course, in a relational database, the result to a query may be sorted in some way for convenience in presentation, such as sorting department members by salary, but logically speaking, the result is still simply a set, ...
Gradual distributed realcoded genetic algorithms
 151 Z. Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs
, 1999
"... Abstract—A major problem in the use of genetic algorithms is premature convergence, a premature stagnation of the search caused by the lack of diversity in the population. One approach for dealing with this problem is the distributed genetic algorithm model. Its basic idea is to keep, in parallel, s ..."
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Cited by 49 (7 self)
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Abstract—A major problem in the use of genetic algorithms is premature convergence, a premature stagnation of the search caused by the lack of diversity in the population. 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. Furthermore, a migration mechanism produces a chromosome exchange between the subpopulations. Making distinctions between the subpopulations by applying genetic algorithms with different configurations, we obtain the socalled heterogeneous distributed genetic algorithms. These algorithms represent a promising way for introducing a correct exploration/exploitation balance in order to avoid premature convergence and reach approximate final solutions. This paper presents the gradual distributed realcoded genetic algorithms, a type of heterogeneous distributed realcoded genetic algorithms that apply a different crossover operator to each subpopulation. The importance of this operator on the genetic algorithm’s performance allowed us to differentiate between the subpopulations in this fashion. Using crossover operators presented for realcoded genetic algorithms, we implement three instances of gradual distributed realcoded genetic algorithms. Experimental results show that the proposals consistently outperform sequential realcoded genetic algorithms and homogeneous distributed realcoded genetic algorithms, which are equivalent to them and other mechanisms presented in the literature. These proposals offer two important advantages at the same time: better reliability and accuracy. Index Terms—Crossover operator, distributed genetic algorithms, multiresolution, premature convergence, selective pressure. I.
Fuzzy Connectives Based Crossover Operators to Model Genetic Algorithms Population Diversity
, 1995
"... Genetic algorithms are adaptive methods which may be used to solve search and optimization problems. Genetic algorithms process a population of search space solutions with three operations: selection, crossover and mutation. An important problem in the use of genetic algorithms is the premature conv ..."
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Cited by 39 (24 self)
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Genetic algorithms are adaptive methods which may be used to solve search and optimization problems. Genetic algorithms process a population of search space solutions with three operations: selection, crossover and mutation. An important problem in the use of genetic algorithms is the premature convergence in a local optimum. Their main causes are the lack of diversity in the population and the disproportionate relationship between exploitation and exploration. The crossover operator is considered one of the most determinant elements for solving this problem. In this paper, we present new crossover operators based on fuzzy connectives for realcoded genetic algorithms. These operators are designed to avoid the premature convergence problem. To do so, they should keep the right exploitation/exploration balance to suitably model the diversity of the population.
A Neurofuzzy scheme for simultaneous feature selection and fuzzy rulebased classification”,IEEE
 Transactions on Neural Networks
, 2004
"... Abstract—Most methods of classification either ignore feature analysis or do it in a separate phase, offline prior to the main classification task. This paper proposes a neurofuzzy scheme for designing a classifier along with feature selection. It is a fourlayered feedforward network for realiz ..."
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Cited by 18 (0 self)
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Abstract—Most methods of classification either ignore feature analysis or do it in a separate phase, offline prior to the main classification task. This paper proposes a neurofuzzy scheme for designing a classifier along with feature selection. It is a fourlayered feedforward network for realizing a fuzzy rulebased classifier. The network is trained by error backpropagation in three phases. In the first phase, the network learns the important features and the classification rules. In the subsequent phases, the network is pruned to an “optimal ” architecture that represents an “optimal” set of rules. Pruning is found to drastically reduce the size of the network without degrading the performance. The pruned network is further tuned to improve performance. The rules learned by the network can be easily read from the network. The system is tested on both synthetic and real data sets and found to perform quite well. Index Terms—Classification, feature analysis, neurofuzzy systems, rule extraction. I.
Fuzzy Tools to Improve Genetic Algorithms
 PROC. OF THE SECOND EUROPEAN CONGRESS ON INTELLIGENT TECHNIQUES AND SOFT COMPUTING
, 1994
"... We propose two fuzzy tools to improve the genetic algorithms behaviour. The use of fuzzy connectives to design crossover operators for real coded genetic algorithms (RCGA), and the use of fuzzy logic based systems for the dynamic control parameters of RCGA based on diversity measures between chromos ..."
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Cited by 16 (8 self)
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We propose two fuzzy tools to improve the genetic algorithms behaviour. The use of fuzzy connectives to design crossover operators for real coded genetic algorithms (RCGA), and the use of fuzzy logic based systems for the dynamic control parameters of RCGA based on diversity measures between chromosomes and alleles.
Appropriate Choice of Aggregation Operators in Fuzzy Decision Support Systems
, 2001
"... Fuzzy logic provides a mathematical formalism for a unified treatment of vagueness and imprecision that are ever present in decision support and expert systems in many areas. The choice of aggregation operators is crucial to the behavior of the system that is intended to mimic human decision making. ..."
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Cited by 14 (2 self)
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Fuzzy logic provides a mathematical formalism for a unified treatment of vagueness and imprecision that are ever present in decision support and expert systems in many areas. The choice of aggregation operators is crucial to the behavior of the system that is intended to mimic human decision making. This paper discusses how aggregation operators can be selected and adjusted to fit empirical dataa series of test cases. Both parametric and nonparametric regression are considered and compared. A practical application of the proposed methods to electronic implementation of clinical guidelines is presented.
Adaptive Genetic Algorithms Based on Fuzzy Techniques
 In Proc. of IPMU'96
, 1996
"... The genetic algorithm behaviour is determined by the exploitation and exploration relationship kept throughout the run. Adaptive genetic algorithms have been built for inducing suitable exploitation/exploration relationships for avoiding the premature convergence problem. Some adaptive genetic algor ..."
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Cited by 10 (0 self)
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The genetic algorithm behaviour is determined by the exploitation and exploration relationship kept throughout the run. Adaptive genetic algorithms have been built for inducing suitable exploitation/exploration relationships for avoiding the premature convergence problem. Some adaptive genetic algorithms are built using fuzzy logic techniques. In this paper, we summarize two types of such approaches. The first one concerns dynamic crossover operators based on parameterized fuzzy connectives and the second one deals with adaptive realcoded genetic algorithms based on the use of fuzzy logic controllers. 1 INTRODUCTION GA behaviour is strongly determined by the balance between exploiting what already works best and exploring possibilities that might eventually evolve into something even better. The loss of critical alleles due to selection pressure, the selection noise, the schemata disruption due to crossover operator, and poor parameter setting may make this exploitation/exploration r...
CIXL2: A Crossover Operator for Evolutionary Algorithms Based on Population Features
 Journal of Artificial Intelligence Research (JAIR
, 2005
"... In this paper we propose a crossover operator for evolutionary algorithms with real values that is based on the statistical theory of population distributions. The operator is based on the theoretical distribution of the values of the genes of the best individuals in the population. The proposed ope ..."
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Cited by 9 (2 self)
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In this paper we propose a crossover operator for evolutionary algorithms with real values that is based on the statistical theory of population distributions. The operator is based on the theoretical distribution of the values of the genes of the best individuals in the population. The proposed operator takes into account the localization and dispersion features of the best individuals of the population with the objective that these features would be inherited by the offspring. Our aim is the optimization of the balance between exploration and exploitation in the search process. In order to test the efficiency and robustness of this crossover, we have used a set of functions to be optimized with regard to different criteria, such as, multimodality, separability, regularity and epistasis. With this set of functions we can extract conclusions in function of the problem at hand. We analyze the results using ANOVA and multiple comparison statistical tests. As an example of how our crossover can be used to solve artificial intelligence problems, we have applied the proposed model to the problem of obtaining the weight of each network in a ensemble of neural networks. The results obtained are above the performance of standard methods. 1.
Searching for Basic Properties Obtaining Robust Implication Operators in Fuzzy Control
 in Fuzzy Control, Fuzzy Sets and Systems 111
, 1998
"... This paper deals with the problem of searching basic properties for robust implication operators in fuzzy control. We use the word "robust" in the sense of good average behavior in different applications and in combination with different defuzzification methods. We study the behavior of th ..."
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Cited by 7 (6 self)
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This paper deals with the problem of searching basic properties for robust implication operators in fuzzy control. We use the word "robust" in the sense of good average behavior in different applications and in combination with different defuzzification methods. We study the behavior of the two main families of implication operators in the fuzzy control inference process. These two families are composed by those operators that extend the boolean implication (implication functions) and those ones that extend the boolean conjunction (tnorms and forceimplications) . In order to develop the comparative study, we will build different fuzzy controllers by means of these implication operators and will apply them to the fuzzy modeling of the real function Y=X and two threedimensional surfaces. We analyze whether one of these two properties, extension of the boolean implication and extension of the boolean conjunction, is sufficient for obtaining a good implication operator or whether some co...