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2019•SoftwareXOpen access

GenConstraint: A programming tool for constraint optimization problems

Ioannis G. Tsoulos, Vasileios T. Stavrou, Nikolaos E. Mastorakis, Dimitrios Tsalikakis

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Abstract

This article presents a software used to solve constrained optimization problems with a modified genetic algorithm, which utilizes a series of modified genetic operators to preserve the feasibility of trial solutions and terminates using a stochastic stopping rule. The software is written entirely in ANSI-C++ and the user can prepare the objective function either in C++ or in Fortran. The article presents the genetic algorithm, the incorporated software as well as some experiments on a series of optimization problems. Also, the proposed software was tested on the design of a two-dimensional filter. The results are compared against the results from the algorithm DONLP2.

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What this paper is about

This article presents a software used to solve constrained optimization problems with a modified genetic algorithm, which utilizes a series of modified genetic operators to preserve the feasibility of trial solutions and terminates using a stochastic stopping rule. The software is written entirely in ANSI-C++ and the user can prepare the objective function either in C++ or in Fortran. The article presents the genetic algorithm, the incorporated software as well as some experiments on a series of optimization problems. Also, the proposed software was tested on the design of a two-dimensional filter. The results are compared against the results from the algorithm DONLP2.

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OpenAlex reports 19 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

This article presents a software used to solve constrained optimization problems with a modified genetic algorithm, which utilizes a series of modified genetic operators to preserve the feasibility of trial solutions and terminates using a stochastic stopping rule. The software is written entirely in ANSI-C++ and the user can prepare the objective function either in C++ or in Fortran. The article presents the genetic algorithm, the incorporated software as well as some experiments on a series of optimization problems. Also, the proposed software was tested on the design of a two-dimensional filter. The results are compared against the results from the algorithm DONLP2.

Key concepts: Computer science, Software, Series (stratigraphy), Fortran, Genetic algorithm, Mathematical optimization, Filter (signal processing), Optimization problem

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