The Experts below are selected from a list of 5337 Experts worldwide ranked by ideXlab platform
Francisco Herrera - One of the best experts on this subject based on the ideXlab platform.
-
a taxonomy for the crossover operator for real coded genetic algorithms an experimental study
International Journal of Intelligent Systems, 2003Co-Authors: Francisco Herrera, M Lozano, Ana M SanchezAbstract:The main real-coded genetic algorithm (RCGA) research effort has been spent on developing efficient crossover operators. This study presents a taxonomy for this operator that groups its instances in different categories according to the way they generate the genes of the offspring from the genes of the parents. The empirical study of representative crossovers of all the categories reveals concrete features that allow the crossover operator to have a positive influence on RCGA performance. They may be useful to design more effective crossover models. © 2003 Wiley Periodicals, Inc. Genetic algorithms (GAs) are adaptive methods based on natural evolution that may be used for search and optimization problems. They process a population of search space solutions with three operations: selection, crossover, and mutation. 1‐3 Under their initial formulation, the search space solutions are coded using the Binary Alphabet; however, other coding types have been taken into account for the representation issue such as real coding. The real coding approach seems particularly natural when tackling optimization problems of parameters with variables in continuous domains. A chromosome is a vector of floating point numbers in which their size is kept the same as the length of the vector, which is the solution to the problem. GAs based on real-number representation are called real-coded GAs
-
Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
Artificial Intelligence Review, 1998Co-Authors: Francisco Herrera, M Lozano, José Luis VerdegayAbstract:Genetic algorithms play a significant role, as search techniques forhandling 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 naturalevolution principles of populations. These algorithms process apopulation 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 real-coded 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.
Tsachy Weissman - One of the best experts on this subject based on the ideXlab platform.
-
information divergences and the curious case of the Binary Alphabet
International Symposium on Information Theory, 2014Co-Authors: Jiantao Jiao, Kartik Venkat, Thomas A Courtade, Tsachy WeissmanAbstract:Four problems related to information divergence measures defined on finite Alphabets are considered. In three of the cases we consider, we illustrate a contrast which arises between the Binary-Alphabet and larger-Alphabet settings. This is surprising in some instances, since characterizations for the larger-Alphabet settings do not generalize their Binary-Alphabet counterparts. For example, we show that f -divergences are not the unique decomposable divergences on Binary Alphabets that satisfy the data processing inequality, despite contrary claims in the literature.
-
information measures the curious case of the Binary Alphabet
arXiv: Information Theory, 2014Co-Authors: Jiantao Jiao, Kartik Venkat, Thomas A Courtade, Tsachy WeissmanAbstract:Four problems related to information divergence measures defined on finite Alphabets are considered. In three of the cases we consider, we illustrate a contrast which arises between the Binary-Alphabet and larger-Alphabet settings. This is surprising in some instances, since characterizations for the larger-Alphabet settings do not generalize their Binary-Alphabet counterparts. Specifically, we show that $f$-divergences are not the unique decomposable divergences on Binary Alphabets that satisfy the data processing inequality, thereby clarifying claims that have previously appeared in the literature. We also show that KL divergence is the unique Bregman divergence which is also an $f$-divergence for any Alphabet size. We show that KL divergence is the unique Bregman divergence which is invariant to statistically sufficient transformations of the data, even when non-decomposable divergences are considered. Like some of the problems we consider, this result holds only when the Alphabet size is at least three.
-
Information Measures: The Curious Case of the Binary Alphabet
IEEE Transactions on Information Theory, 2014Co-Authors: Jiantao Jiao, Kartik Venkat, Albert No, Thomas A Courtade, Tsachy WeissmanAbstract:Four problems related to information divergence measures defined on finite Alphabets are considered. In three of the cases we consider, we illustrate a contrast that arises between the Binary-Alphabet and larger Alphabet settings. This is surprising in some instances, since characterizations for the larger Alphabet settings do not generalize their Binary-Alphabet counterparts. In particular, we show that f-divergences are not the unique decomposable divergences on Binary Alphabets that satisfy the data processing inequality, thereby clarifying claims that have previously appeared in the literature. We also show that Kullback-Leibler (KL) divergence is the unique Bregman divergence, which is also an f-divergence for any Alphabet size. We show that KL divergence is the unique Bregman divergence, which is invariant to statistically sufficient transformations of the data, even when nondecomposable divergences are considered. Like some of the problems we consider, this result holds only when the Alphabet size is at least three.
José Luis Verdegay - One of the best experts on this subject based on the ideXlab platform.
-
Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
Artificial Intelligence Review, 1998Co-Authors: Francisco Herrera, M Lozano, José Luis VerdegayAbstract:Genetic algorithms play a significant role, as search techniques forhandling 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 naturalevolution principles of populations. These algorithms process apopulation 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 real-coded 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.
M Lozano - One of the best experts on this subject based on the ideXlab platform.
-
a taxonomy for the crossover operator for real coded genetic algorithms an experimental study
International Journal of Intelligent Systems, 2003Co-Authors: Francisco Herrera, M Lozano, Ana M SanchezAbstract:The main real-coded genetic algorithm (RCGA) research effort has been spent on developing efficient crossover operators. This study presents a taxonomy for this operator that groups its instances in different categories according to the way they generate the genes of the offspring from the genes of the parents. The empirical study of representative crossovers of all the categories reveals concrete features that allow the crossover operator to have a positive influence on RCGA performance. They may be useful to design more effective crossover models. © 2003 Wiley Periodicals, Inc. Genetic algorithms (GAs) are adaptive methods based on natural evolution that may be used for search and optimization problems. They process a population of search space solutions with three operations: selection, crossover, and mutation. 1‐3 Under their initial formulation, the search space solutions are coded using the Binary Alphabet; however, other coding types have been taken into account for the representation issue such as real coding. The real coding approach seems particularly natural when tackling optimization problems of parameters with variables in continuous domains. A chromosome is a vector of floating point numbers in which their size is kept the same as the length of the vector, which is the solution to the problem. GAs based on real-number representation are called real-coded GAs
-
Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
Artificial Intelligence Review, 1998Co-Authors: Francisco Herrera, M Lozano, José Luis VerdegayAbstract:Genetic algorithms play a significant role, as search techniques forhandling 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 naturalevolution principles of populations. These algorithms process apopulation 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 real-coded 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.
Jiantao Jiao - One of the best experts on this subject based on the ideXlab platform.
-
information divergences and the curious case of the Binary Alphabet
International Symposium on Information Theory, 2014Co-Authors: Jiantao Jiao, Kartik Venkat, Thomas A Courtade, Tsachy WeissmanAbstract:Four problems related to information divergence measures defined on finite Alphabets are considered. In three of the cases we consider, we illustrate a contrast which arises between the Binary-Alphabet and larger-Alphabet settings. This is surprising in some instances, since characterizations for the larger-Alphabet settings do not generalize their Binary-Alphabet counterparts. For example, we show that f -divergences are not the unique decomposable divergences on Binary Alphabets that satisfy the data processing inequality, despite contrary claims in the literature.
-
information measures the curious case of the Binary Alphabet
arXiv: Information Theory, 2014Co-Authors: Jiantao Jiao, Kartik Venkat, Thomas A Courtade, Tsachy WeissmanAbstract:Four problems related to information divergence measures defined on finite Alphabets are considered. In three of the cases we consider, we illustrate a contrast which arises between the Binary-Alphabet and larger-Alphabet settings. This is surprising in some instances, since characterizations for the larger-Alphabet settings do not generalize their Binary-Alphabet counterparts. Specifically, we show that $f$-divergences are not the unique decomposable divergences on Binary Alphabets that satisfy the data processing inequality, thereby clarifying claims that have previously appeared in the literature. We also show that KL divergence is the unique Bregman divergence which is also an $f$-divergence for any Alphabet size. We show that KL divergence is the unique Bregman divergence which is invariant to statistically sufficient transformations of the data, even when non-decomposable divergences are considered. Like some of the problems we consider, this result holds only when the Alphabet size is at least three.
-
Information Measures: The Curious Case of the Binary Alphabet
IEEE Transactions on Information Theory, 2014Co-Authors: Jiantao Jiao, Kartik Venkat, Albert No, Thomas A Courtade, Tsachy WeissmanAbstract:Four problems related to information divergence measures defined on finite Alphabets are considered. In three of the cases we consider, we illustrate a contrast that arises between the Binary-Alphabet and larger Alphabet settings. This is surprising in some instances, since characterizations for the larger Alphabet settings do not generalize their Binary-Alphabet counterparts. In particular, we show that f-divergences are not the unique decomposable divergences on Binary Alphabets that satisfy the data processing inequality, thereby clarifying claims that have previously appeared in the literature. We also show that Kullback-Leibler (KL) divergence is the unique Bregman divergence, which is also an f-divergence for any Alphabet size. We show that KL divergence is the unique Bregman divergence, which is invariant to statistically sufficient transformations of the data, even when nondecomposable divergences are considered. Like some of the problems we consider, this result holds only when the Alphabet size is at least three.