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Syracuse University

Genetic algorithms

Electrical Engineering and Computer Science - Technical Reports

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Full-Text Articles in Physical Sciences and Mathematics

Genetic Algorithms For Soft Decision Decoding Of Linear Block Codes, Harpal Maini, Kishan Mehrotra, Chilukuri K. Mohan, Sanjay Ranka Nov 1993

Genetic Algorithms For Soft Decision Decoding Of Linear Block Codes, Harpal Maini, Kishan Mehrotra, Chilukuri K. Mohan, Sanjay Ranka

Electrical Engineering and Computer Science - Technical Reports

Soft-decision decoding is an NP-hard problem of great interest to developers of communication systems. We show that this problem is equivalent to the problem of optimizing Walsh polynomials. We present genetic algorithms for soft-decision decoding of binary linear block codes and compare the performance with various other decoding algorithms. Simulation results show that our algorithms achieve bit-error-probabilities as low as 0.00183 for a [104, 52] code with a low signal-to-noise ratio of 2.5 dB, exploring only 30,000 codewords, whereas the search space contains 4.5 x 1015 codewords. We define a new crossover operator that exploits domain-specific information and compare it …


A Comparison Of Load Balancing Algorithms For Parallel Computations, N. Mansouri, Geoffrey C. Fox Sep 1991

A Comparison Of Load Balancing Algorithms For Parallel Computations, N. Mansouri, Geoffrey C. Fox

Electrical Engineering and Computer Science - Technical Reports

Three physical optimization methods are considered in this paper for load balancing parallel computations. These are simulated annealing, genetic algorithms, and neural networks. Some design choices and the inclusion of additional steps lead to new versions of the algorithms with different solution qualities and execution times. The performances of these versions are critically evaluated and compared for test cases with different topologies and sizes. Orthogonal recursive coordinate bisection is also included in the comparison as a typical simple deterministic method. Simulation results show that the algorithms have diverse properties. Hence, different algorithms can be applied to different problems and requirements. …