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147,872
The Nature of Statistical Learning Theory
, 1999
"... Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based on the deve ..."
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Cited by 13236 (32 self)
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Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based
A New Genetic Algorithm for the Optimal
 In Fonlupt
, 1999
"... This paper proposes a new genetic algorithm to solve the Optimal Communication Spanning Tree problem. The proposed algorithm works on a tree chromosome without intermediate encoding and decoding, and uses crossovers and mutations which manipulate directly trees, while a traditional genetic algor ..."
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This paper proposes a new genetic algorithm to solve the Optimal Communication Spanning Tree problem. The proposed algorithm works on a tree chromosome without intermediate encoding and decoding, and uses crossovers and mutations which manipulate directly trees, while a traditional genetic
A New Genetic Algorithm For VRPTW
 Proceedings of the International Conference on Artificial Intelligence
, 2000
"... This paper presents a new genetic algorithm to solve Vehicle Routing Problem with Time Windows (VRPTW) to near optimal solutions. The objective of the problem is to serve a number of customers within predefined time windows at minimum cost, without violating the capacity and total trip time constrai ..."
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Cited by 11 (0 self)
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This paper presents a new genetic algorithm to solve Vehicle Routing Problem with Time Windows (VRPTW) to near optimal solutions. The objective of the problem is to serve a number of customers within predefined time windows at minimum cost, without violating the capacity and total trip time
Predictive Models for the Breeder Genetic Algorithm  I. Continuous Parameter Optimization
 EVOLUTIONARY COMPUTATION
, 1993
"... In this paper a new genetic algorithm called the Breeder Genetic Algorithm (BGA) is introduced. The BGA is based on artificial selection similar to that used by human breeders. A predictive model for the BGA is presented which is derived from quantitative genetics. The model is used to predict t ..."
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Cited by 400 (25 self)
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In this paper a new genetic algorithm called the Breeder Genetic Algorithm (BGA) is introduced. The BGA is based on artificial selection similar to that used by human breeders. A predictive model for the BGA is presented which is derived from quantitative genetics. The model is used to predict
Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization
, 1993
"... The paper describes a rankbased fitness assignment method for Multiple Objective Genetic Algorithms (MOGAs). Conventional niche formation methods are extended to this class of multimodal problems and theory for setting the niche size is presented. The fitness assignment method is then modified to a ..."
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Cited by 633 (15 self)
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The paper describes a rankbased fitness assignment method for Multiple Objective Genetic Algorithms (MOGAs). Conventional niche formation methods are extended to this class of multimodal problems and theory for setting the niche size is presented. The fitness assignment method is then modified
Experiments with a New Boosting Algorithm
, 1996
"... In an earlier paper, we introduced a new “boosting” algorithm called AdaBoost which, theoretically, can be used to significantly reduce the error of any learning algorithm that consistently generates classifiers whose performance is a little better than random guessing. We also introduced the relate ..."
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Cited by 2213 (20 self)
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In an earlier paper, we introduced a new “boosting” algorithm called AdaBoost which, theoretically, can be used to significantly reduce the error of any learning algorithm that consistently generates classifiers whose performance is a little better than random guessing. We also introduced
Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms
 Evolutionary Computation
, 1994
"... In trying to solve multiobjective optimization problems, many traditional methods scalarize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands the user to have knowledge about t ..."
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Cited by 539 (5 self)
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the underlying problem. Moreover, in solving multiobjective problems, designers may be interested in a set of Paretooptimal points, instead of a single point. Since genetic algorithms(GAs) work with a population of points, it seems natural to use GAs in multiobjective optimization problems to capture a
Optimization of Fidelity in Digital Image Watermarking Using a New Genetic Algorithm
, 2012
"... Abstract In this paper we present a New Genetic Algorithm based on Center of Mass Selection Operator (CMGA), to find the optimum locations for digital watermark insertion in a cover image, by focusing on the issue of optimizing Fidelity. The various existing techniques based on spatial and frequenc ..."
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Abstract In this paper we present a New Genetic Algorithm based on Center of Mass Selection Operator (CMGA), to find the optimum locations for digital watermark insertion in a cover image, by focusing on the issue of optimizing Fidelity. The various existing techniques based on spatial
Genetic Programming
, 1997
"... Introduction Genetic programming is a domainindependent problemsolving approach in which computer programs are evolved to solve, or approximately solve, problems. Genetic programming is based on the Darwinian principle of reproduction and survival of the fittest and analogs of naturally occurring ..."
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Cited by 1056 (12 self)
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is now called the genetic algorithm (GA). The genetic algorithm attempts to find a good (or best) solution to the problem by genetically breeding a population of individuals over a series of generations. In the genetic algorithm, each individual in the population represents a candidate solut
A new learning algorithm for blind signal separation

, 1996
"... A new online learning algorithm which minimizes a statistical dependency among outputs is derived for blind separation of mixed signals. The dependency is measured by the average mutual information (MI) of the outputs. The source signals and the mixing matrix are unknown except for the number of ..."
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Cited by 622 (80 self)
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A new online learning algorithm which minimizes a statistical dependency among outputs is derived for blind separation of mixed signals. The dependency is measured by the average mutual information (MI) of the outputs. The source signals and the mixing matrix are unknown except for the number
Results 1  10
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147,872