Results 1 - 10 of 1,340,671

Optimizing for Reduced Code Space Using Genetic Algorithms

by Keith D. Cooper, Philip J. Schielke, Devika Subramanian , 1999
"... Code space is a critical issue facing designers of software for embedded systems. Many traditional compiler optimizations are designed to reduce the execution time of compiled code, but not necessarily the size of the compiled code. Further, di#erent results can be achieved by running some optimizat ..."
Abstract - Cited by 162 (11 self) - Add to MetaCart
Code space is a critical issue facing designers of software for embedded systems. Many traditional compiler optimizations are designed to reduce the execution time of compiled code, but not necessarily the size of the compiled code. Further, di#erent results can be achieved by running some

Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms

by N. Srinivas, Kalyanmoy Deb - 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 ..."
Abstract - Cited by 524 (4 self) - Add to MetaCart
the underlying problem. Moreover, in solving multiobjective problems, designers may be interested in a set of Pareto-optimal 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

Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization

by Carlos M. Fonseca, Peter J. Fleming , 1993
"... The paper describes a rank-based 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 ..."
Abstract - Cited by 610 (15 self) - Add to MetaCart
to allow direct intervention of an external decision maker (DM). Finally, the MOGA is generalised further: the genetic algorithm is seen as the optimizing element of a multiobjective optimization loop, which also comprises the DM. It is the interaction between the two that leads to the determination of a

A comparative analysis of selection schemes used in genetic algorithms

by David E. Goldberg, Kalyanmoy Deb - Foundations of Genetic Algorithms , 1991
"... This paper considers a number of selection schemes commonly used in modern genetic algorithms. Specifically, proportionate reproduction, ranking selection, tournament selection, and Genitor (or «steady state") selection are compared on the basis of solutions to deterministic difference or d ..."
Abstract - Cited by 512 (32 self) - Add to MetaCart
This paper considers a number of selection schemes commonly used in modern genetic algorithms. Specifically, proportionate reproduction, ranking selection, tournament selection, and Genitor (or «steady state") selection are compared on the basis of solutions to deterministic difference

Particle swarm optimization

by James Kennedy, Russell Eberhart , 1995
"... eberhart @ engr.iupui.edu A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications ..."
Abstract - Cited by 3535 (22 self) - Add to MetaCart
, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described, 1

Space-time block codes from orthogonal designs

by Vahid Tarokh, Hamid Jafarkhani, A. R. Calderbank - IEEE Trans. Inform. Theory , 1999
"... Abstract — We introduce space–time block coding, a new paradigm for communication over Rayleigh fading channels using multiple transmit antennas. Data is encoded using a space–time block code and the encoded data is split into � streams which are simultaneously transmitted using � transmit antennas. ..."
Abstract - Cited by 1509 (42 self) - Add to MetaCart
Abstract — We introduce space–time block coding, a new paradigm for communication over Rayleigh fading channels using multiple transmit antennas. Data is encoded using a space–time block code and the encoded data is split into � streams which are simultaneously transmitted using � transmit antennas

A Fast and Elitist Multi-Objective Genetic Algorithm: NSGA-II

by Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, T. Meyarivan , 2000
"... Multi-objective evolutionary algorithms which use non-dominated sorting and sharing have been mainly criticized for their (i) O(MN computational complexity (where M is the number of objectives and N is the population size), (ii) non-elitism approach, and (iii) the need for specifying a sharing param ..."
Abstract - Cited by 1707 (58 self) - Add to MetaCart
Multi-objective evolutionary algorithms which use non-dominated sorting and sharing have been mainly criticized for their (i) O(MN computational complexity (where M is the number of objectives and N is the population size), (ii) non-elitism approach, and (iii) the need for specifying a sharing

A Limited Memory Algorithm for Bound Constrained Optimization

by Richard H. Byrd, Richard H. Byrd, Peihuang Lu, Peihuang Lu, Jorge Nocedal, Jorge Nocedal, Ciyou Zhu, Ciyou Zhu - SIAM Journal on Scientific Computing , 1994
"... An algorithm for solving large nonlinear optimization problems with simple bounds is described. ..."
Abstract - Cited by 557 (9 self) - Add to MetaCart
An algorithm for solving large nonlinear optimization problems with simple bounds is described.

A training algorithm for optimal margin classifiers

by Bernhard E. Boser, et al. - PROCEEDINGS OF THE 5TH ANNUAL ACM WORKSHOP ON COMPUTATIONAL LEARNING THEORY , 1992
"... A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of classifiaction functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters is adjust ..."
Abstract - Cited by 1848 (44 self) - Add to MetaCart
A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of classifiaction functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters

SNOPT: An SQP Algorithm For Large-Scale Constrained Optimization

by Philip E. Gill, Walter Murray, Michael A. Saunders , 2002
"... Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first deriv ..."
Abstract - Cited by 582 (23 self) - Add to MetaCart
Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first
Results 1 - 10 of 1,340,671