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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 158 (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

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 1524 (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

Iterative decoding of binary block and convolutional codes

by Joachim Hagenauer, Elke Offer, Lutz Papke - IEEE TRANS. INFORM. THEORY , 1996
"... Iterative decoding of two-dimensional systematic convolutional codes has been termed “turbo” (de)coding. Using log-likelihood algebra, we show that any decoder can he used which accepts soft inputs-including a priori values-and delivers soft outputs that can he split into three terms: the soft chann ..."
Abstract - Cited by 610 (43 self) - Add to MetaCart
channel and a priori inputs, and the extrinsic value. The extrinsic value is used as an a priori value for the next iteration. Decoding algorithms in the log-likelihood domain are given not only for convolutional codes hut also for any linear binary systematic block code. The iteration is controlled by a

Software pipelining: An effective scheduling technique for VLIW machines

by Monica Lam , 1988
"... This paper shows that software pipelining is an effective and viable scheduling technique for VLIW processors. In software pipelining, iterations of a loop in the source program are continuously initiated at constant intervals, before the preceding iterations complete. The advantage of software pipe ..."
Abstract - Cited by 581 (3 self) - Add to MetaCart
pipelining is that optimal performance can be achieved with compact object code. This paper extends previous results of software pipelining in two ways: First, this paper shows that by using an im-proved algorithm, near-optimal performance can be obtained without specialized hardware. Second, we propose a

Benchmarking Least Squares Support Vector Machine Classifiers

by Tony Van Gestel, Johan A. K. Suykens, Bart Baesens, Stijn Viaene, Jan Vanthienen, Guido Dedene, Bart De Moor, Joos Vandewalle - NEURAL PROCESSING LETTERS , 2001
"... In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a (convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LS-SVMs), a least squares cost function is proposed so as to obtain a linear set of eq ..."
Abstract - Cited by 476 (46 self) - Add to MetaCart
problems are represented by a set of binary classifiers using different output coding schemes. While regularization is used to control the effective number of parameters of the LS-SVM classifier, the sparseness property of SVMs is lost due to the choice of the 2-norm. Sparseness can be imposed in a second

A Survey of Optimization by Building and Using Probabilistic Models

by Martin Pelikan, David E. Goldberg, Fernando Lobo - COMPUTATIONAL OPTIMIZATION AND APPLICATIONS , 1999
"... This paper summarizes the research on population-based probabilistic search algorithms based on modeling promising solutions by estimating their probability distribution and using the constructed model to guide the further exploration of the search space. It settles the algorithms in the field of ge ..."
Abstract - Cited by 339 (90 self) - Add to MetaCart
This paper summarizes the research on population-based probabilistic search algorithms based on modeling promising solutions by estimating their probability distribution and using the constructed model to guide the further exploration of the search space. It settles the algorithms in the field

Data privacy through optimal k-anonymization

by Roberto J. Bayardo - In ICDE , 2005
"... Data de-identification reconciles the demand for release of data for research purposes and the demand for privacy from individuals. This paper proposes and evaluates an optimization algorithm for the powerful de-identification procedure known as k-anonymization. A k-anonymized dataset has the proper ..."
Abstract - Cited by 344 (3 self) - Add to MetaCart
produce good anonymizations in circumstances where the input data or input parameters preclude finding an optimal solution in reasonable time. Finally, we use the algorithm to explore the effects of different coding approaches and problem variations on anonymization quality and performance. To our

Approximating the nondominated front using the Pareto Archived Evolution Strategy

by Joshua D. Knowles, David W. Corne - EVOLUTIONARY COMPUTATION , 2000
"... We introduce a simple evolution scheme for multiobjective optimization problems, called the Pareto Archived Evolution Strategy (PAES). We argue that PAES may represent the simplest possible nontrivial algorithm capable of generating diverse solutions in the Pareto optimal set. The algorithm, in its ..."
Abstract - Cited by 321 (19 self) - Add to MetaCart
of the Niched Pareto Genetic Algorithm and the Nondominated Sorting Genetic Algorithm over a diverse suite of six test functions. Results are analyzed and presented using techniques that reduce the attainment surfaces generated from several optimization runs into a set of univariate distributions. This allows

Tackling real-coded genetic algorithms: operators and tools for the behavioural analysis

by F. Herrera, M. Lozano, J. L. Verdegay - Arti Intelligence Reviews , 1998
"... Abstract. Genetic algorithms play a significant role, as search techniques for handling com-plex 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 ..."
Abstract - Cited by 198 (27 self) - Add to MetaCart
principles of populations. These algorithms process a population of chromo-somes, 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

Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms

by Terry Jones, Stephanie Forrest - Proceedings of the Sixth International Conference on Genetic Algorithms , 1995
"... A measure of search difficulty, fitness distance correlation (FDC), is introduced and examined in relation to genetic algorithm (GA) performance. In many cases, this correlation can be used to predict the performance of a GA on problems with known global maxima. It correctly classifies easy deceptiv ..."
Abstract - Cited by 258 (5 self) - Add to MetaCart
A measure of search difficulty, fitness distance correlation (FDC), is introduced and examined in relation to genetic algorithm (GA) performance. In many cases, this correlation can be used to predict the performance of a GA on problems with known global maxima. It correctly classifies easy
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