Addressing Constrained Sampling Optimization Problems Using Evolutionary Algorithms
In this work we address the solution of a particular category of problems, denoted as Constrained Sampling problems, using evolution. These problems have not usually been addressed using EAs. They are characterized by the fact that the fitness landscape evaluation is not always straightforward due to the computational cost or to physical constraints of the specific application. The decoding phase of these problems usually implies some type of physical migration from the constructs generated to obtain the fitness of the parents towards those required to obtain the fitness of the offspring. As a consequence, it is not instantaneous and requires a series of steps. Most traditional evolutionary algorithms ignore the information on the fitness landscape that can be obtained from these intermediate steps. We propose a series of modifications that can be applied to most EAs that allow improving their efficiency when applied to this type of problems. This approach has been tested using some common real-coded benchmark functions and its performance compared to that of a standard EA, specifically a Differential Evolution algorithm.
KeywordsOptimization Evolutionary Algorithms Constrained Sampling Problems Constrained Sampling Evolutionary Algorithm
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