Abstract
This paper presents research into an evolutionary algorithm that utilizes the coevolution of feasible and infeasible solutions in solving constrained optimization problems. The evolution of these populations occurs through the use of traditional and specially designed operators that allow for crossover to occur in each of the populations as well as across the two populations. The cross population crossover allows for the information contained in the infeasible solutions to be utilized in the search for the optimal solution.
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Albright, H.T., Ignizio, J.P. (1997). An evolutionary algorithm for single objective nonlinear constrained optimization problems. In: Corne, D., Shapiro, J.L. (eds) Evolutionary Computing. AISB EC 1997. Lecture Notes in Computer Science, vol 1305. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0027166
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DOI: https://doi.org/10.1007/BFb0027166
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