We consider the optimization problem of constructing a binary orthogonal array (OA) starting from a bigger one, by removing a specified amount of lines. In particular, we develop a genetic algorithm (GA) where the underlying chromosomes are constant-weight binary strings that specify the lines to be cancelled from the starting OA. Such chromosomes are then evolved through balanced crossover and mutation operators to preserve the number of ones in them. The fitness function evaluates the matrices obtained from these chromosomes by measuring their distance from satisfying the constraints of an OA smaller than the starting one. We perform a preliminary experimental validation of the proposed genetic algorithm by crafting the initial OA as a random permutation of several blocks of the basic parity-check array, thereby guaranteeing the existence of an optimal solution.

Mariot, L. (2021). Deriving Smaller Orthogonal Arrays from Bigger Ones with Genetic Algorithm. Intervento presentato a: 2nd Workshop on Evolutionary and Population-based Optimization Wepo2021 - November 30, 2021, Online Event.

Deriving Smaller Orthogonal Arrays from Bigger Ones with Genetic Algorithm

Mariot, L
2021

Abstract

We consider the optimization problem of constructing a binary orthogonal array (OA) starting from a bigger one, by removing a specified amount of lines. In particular, we develop a genetic algorithm (GA) where the underlying chromosomes are constant-weight binary strings that specify the lines to be cancelled from the starting OA. Such chromosomes are then evolved through balanced crossover and mutation operators to preserve the number of ones in them. The fitness function evaluates the matrices obtained from these chromosomes by measuring their distance from satisfying the constraints of an OA smaller than the starting one. We perform a preliminary experimental validation of the proposed genetic algorithm by crafting the initial OA as a random permutation of several blocks of the basic parity-check array, thereby guaranteeing the existence of an optimal solution.
paper
Computer Science - Neural and Evolutionary Computing; Computer Science - Neural and Evolutionary Computing
English
2nd Workshop on Evolutionary and Population-based Optimization Wepo2021 - November 30, 2021
2021
2021
https://wepo2021.aisylab.com/
reserved
Mariot, L. (2021). Deriving Smaller Orthogonal Arrays from Bigger Ones with Genetic Algorithm. Intervento presentato a: 2nd Workshop on Evolutionary and Population-based Optimization Wepo2021 - November 30, 2021, Online Event.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/501859
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