What Makes a Constrained Problem Difficult to Solve by an Evolutionary Algorithm

An empirical study about the features that prevent an Evolutionary Algorithm to reach the feasible region or even get the global optimum when it is used to solve global optimization constrained optimization problems is presented. For the experiments we use a Simple Multimembered Evolution Strategy which provides very competitive results in the well known benchmark of 13 test functions. Also, we add 11 new problems which have features we hypothesize that decrease the performance of the algorithm (nonlinear equality constraints and dimensionality). The results seems to agree with our idea and they give some insights to develop more robust EA’s for global optimization mainly for real world problems which have the features analyzed in this work.

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