What makes a problem good for an evolutionary strategy vs a genetic algorithm vs particle swarm optimization?












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I understand that evolutionary strategies (ES), genetic algorithms (GA), and particle swarm optimization (PSO) are all algorithms used to solve optimization types of problems, but what might make an optimization problem better to choose one of those techniques over the other?



For example, I read in an article that genetic algorithms are typically preferred for combinatorial problems, but other than that one sentence or so, I haven't been able to find any source that explains when you might choose one of those techniques over the other.



Is there a general approach one might use when trying to decide which technique to use, or is it more of a guess and check type of situation? It would be great if someone could help me understand when each of the three cases might be preferred.










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    $begingroup$


    I understand that evolutionary strategies (ES), genetic algorithms (GA), and particle swarm optimization (PSO) are all algorithms used to solve optimization types of problems, but what might make an optimization problem better to choose one of those techniques over the other?



    For example, I read in an article that genetic algorithms are typically preferred for combinatorial problems, but other than that one sentence or so, I haven't been able to find any source that explains when you might choose one of those techniques over the other.



    Is there a general approach one might use when trying to decide which technique to use, or is it more of a guess and check type of situation? It would be great if someone could help me understand when each of the three cases might be preferred.










    share|improve this question









    New contributor




    wakeel is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.







    $endgroup$















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      $begingroup$


      I understand that evolutionary strategies (ES), genetic algorithms (GA), and particle swarm optimization (PSO) are all algorithms used to solve optimization types of problems, but what might make an optimization problem better to choose one of those techniques over the other?



      For example, I read in an article that genetic algorithms are typically preferred for combinatorial problems, but other than that one sentence or so, I haven't been able to find any source that explains when you might choose one of those techniques over the other.



      Is there a general approach one might use when trying to decide which technique to use, or is it more of a guess and check type of situation? It would be great if someone could help me understand when each of the three cases might be preferred.










      share|improve this question









      New contributor




      wakeel is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.







      $endgroup$




      I understand that evolutionary strategies (ES), genetic algorithms (GA), and particle swarm optimization (PSO) are all algorithms used to solve optimization types of problems, but what might make an optimization problem better to choose one of those techniques over the other?



      For example, I read in an article that genetic algorithms are typically preferred for combinatorial problems, but other than that one sentence or so, I haven't been able to find any source that explains when you might choose one of those techniques over the other.



      Is there a general approach one might use when trying to decide which technique to use, or is it more of a guess and check type of situation? It would be great if someone could help me understand when each of the three cases might be preferred.







      optimization genetic-algorithms evolutionary-algorithms






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      share|improve this question









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      asked 15 mins ago









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