<abstract xmlns="http://www.w3.org/1999/xhtml"><p> Multiobjective optimization based on genetic algorithms and Pareto based approaches in solving multiobjective optimization problems is discussed in the paper. A Pareto based fitness assignment is used − non-dominated ranking and movement of a population towards the Pareto front in a multiobjective optimization problem. A MultiObjective Genetic Modified Algorithm (MOGMA) is proposed, which is an improvement of the existing algorithm.</p></abstract>

Multiobjective optimization based on genetic algorithms and Pareto based approaches in solving multiobjective optimization problems is discussed in the paper. A Pareto based fitness assignment is used − non-dominated ranking and movement of a population towards the Pareto front in a multiobjective optimization problem. A MultiObjective Genetic Modified Algorithm (MOGMA) is proposed, which is an improvement of the existing algorithm.

MultiObjective Genetic Modified Algorithm (MOGMA)

Institute of Information and Communication Technologies, 1113 Sofia

Cybernetics and Information Technologies

{"article-title":"MultiObjective Genetic Modified Algorithm (MOGMA)"}

Multiobjective optimization based on genetic algorithms and Pareto based approaches in solving multiobjective optimization problems is discussed in the paper....

MultiObjective Genetic Modified Algorithm (MOGMA)

Online veröffentlicht: 16. März 2013

Seitenbereich: 23 - 33

DOI: https://doi.org/10.2478/cait-2012-0010

Schlüsselwörter
Multiobjective optimization, genetic algorithms, population, Pareto based approaches, ideal point, ideal function, MultiObjective Genetic Modified Algorithm

This content is open access.

MultiObjective Genetic Modified Algorithm (MOGMA)

Online veröffentlicht: 16. März 2013

Seitenbereich: 23 - 33

DOI: https://doi.org/10.2478/cait-2012-0010

SchlüsselwörterMultiobjective optimization, genetic algorithms, population, Pareto based approaches, ideal point, ideal function, MultiObjective Genetic Modified Algorithm

This content is open access.

Schlüsselwörter
Multiobjective optimization, genetic algorithms, population, Pareto based approaches, ideal point, ideal function, MultiObjective Genetic Modified Algorithm