### Abstract

namely, probabilistic artificial neural network, simulated annealing,

and modified genetic algorithm in finding the minimumnorm-residual

solution to linear systems of equations. By demonstrating

a set of input parameters, the objective function, and the

expected results solutions are computed for determined, overdetermined,

and underdetermined linear systems. In addition, this

work will present a version of genetic algorithm modified in

terms of reproduction and mutation. In this modification, every

reproduction cycle is performed by matching each individual with

the rest of the individuals in the population. Further, the offspring

chromosomes result from crossover of parent chromosomes

without mutation. The selection process only selects the best fit

individuals in the population. Mutation is only performed when

the desired level of fitness cannot be achieved, and all the possible

chromosome combinations were already exhausted. Experimental

results for randorrly generated matrices with increasing matrix

sizes will be presented and analyzed. It will be the basis in

modeling and identifying the dynamics parameters of a humanoid

robot through response optimization at excitatory motions.

Original language | English |
---|---|

Pages (from-to) | 1-9 |

Number of pages | 9 |

Journal | Philippine Computing Journal |

Volume | 4 |

Issue number | 2 |

Publication status | Published - 2009 |

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### Cite this

*Philippine Computing Journal*,

*4*(2), 1-9.

}

*Philippine Computing Journal*, vol. 4, no. 2, pp. 1-9.

**Using Metaheuristic Computations to Find the Minimum-Norm-Residual Solution to Linear Systems of Equations.** / Jamisola, Rodrigo; Dadios, Elmer P.; Ang, Marcelo H.

Research output: Contribution to journal › Article

TY - JOUR

T1 - Using Metaheuristic Computations to Find the Minimum-Norm-Residual Solution to Linear Systems of Equations

AU - Jamisola, Rodrigo

AU - Dadios, Elmer P.

AU - Ang, Marcelo H.

PY - 2009

Y1 - 2009

N2 - This work will present metaheuristic computations,namely, probabilistic artificial neural network, simulated annealing,and modified genetic algorithm in finding the minimumnorm-residualsolution to linear systems of equations. By demonstratinga set of input parameters, the objective function, and theexpected results solutions are computed for determined, overdetermined,and underdetermined linear systems. In addition, thiswork will present a version of genetic algorithm modified interms of reproduction and mutation. In this modification, everyreproduction cycle is performed by matching each individual withthe rest of the individuals in the population. Further, the offspringchromosomes result from crossover of parent chromosomeswithout mutation. The selection process only selects the best fitindividuals in the population. Mutation is only performed whenthe desired level of fitness cannot be achieved, and all the possiblechromosome combinations were already exhausted. Experimentalresults for randorrly generated matrices with increasing matrixsizes will be presented and analyzed. It will be the basis inmodeling and identifying the dynamics parameters of a humanoidrobot through response optimization at excitatory motions.

AB - This work will present metaheuristic computations,namely, probabilistic artificial neural network, simulated annealing,and modified genetic algorithm in finding the minimumnorm-residualsolution to linear systems of equations. By demonstratinga set of input parameters, the objective function, and theexpected results solutions are computed for determined, overdetermined,and underdetermined linear systems. In addition, thiswork will present a version of genetic algorithm modified interms of reproduction and mutation. In this modification, everyreproduction cycle is performed by matching each individual withthe rest of the individuals in the population. Further, the offspringchromosomes result from crossover of parent chromosomeswithout mutation. The selection process only selects the best fitindividuals in the population. Mutation is only performed whenthe desired level of fitness cannot be achieved, and all the possiblechromosome combinations were already exhausted. Experimentalresults for randorrly generated matrices with increasing matrixsizes will be presented and analyzed. It will be the basis inmodeling and identifying the dynamics parameters of a humanoidrobot through response optimization at excitatory motions.

M3 - Article

VL - 4

SP - 1

EP - 9

JO - Philippine Computing Journal

JF - Philippine Computing Journal

SN - 1908-1995

IS - 2

ER -