Abstract
The most general strategy for handling constraints in evolutionary optimization is through penalty functions. The choice of the penalty function is critical to both success and efficiency of the optimization. Many strategies have been proposed for formulating penalty functions, most of which rely on arbitrary tuning of parameters. An new insight on function penalization is proposed in this paper that relies on the dual optimization problem. An evolutionary algorithm for approximately solving dual optimization problems is first presented. Next, an efficient and exact penalty function without penalization parameter to be tuned is proposed. Numerical tests are provided for continuous variables and inequality constraints.
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Riche, R.L., Guyon, F. (2002). Dual Evolutionary Optimization. In: Collet, P., Fonlupt, C., Hao, JK., Lutton, E., Schoenauer, M. (eds) Artificial Evolution. EA 2001. Lecture Notes in Computer Science, vol 2310. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46033-0_23
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DOI: https://doi.org/10.1007/3-540-46033-0_23
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