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|Title:||Constrained evolutionary exploration via genetic structure of packet distribution|
|Authors:||Tan, K.C. |
|Source:||Tan, K.C.,Lee, T.H.,Khoo, D.,Khor, E.F. (2001). Constrained evolutionary exploration via genetic structure of packet distribution. Proceedings of the IEEE Conference on Evolutionary Computation, ICEC 1 : 693-703. ScholarBank@NUS Repository.|
|Abstract:||Many evolutionary algorithm based methods have been proposed for handling constraints in numerical optimization problems during the last few years. These techniques, however, are often based upon the approach of formulating constraints in the objective domain or repairing/rejecting infeasible solutions through specialized genetic operators. The drawback of these approaches is that the potential for both feasible and infeasible solutions coexist, which often leads to a large search space with complex or discontinuous fitness landscape. These infeasible chromosomes must be evaluated or detected with extra computational effort before they are penalized or eliminated from the population. Moreover, these methods need to ensure the domination of feasible candidate solutions during genetic reproductions in order to eliminate the infeasible ones, which can easily misdirect the evolution towards the local optima whenever a feasible solution is reproduced in problems that contain difficult-to-find feasible regions . This paper describes a constraint handling methodology that formulates the optimization constraints directly into the gene domains in evolutionary algorithms. It allows the constraints to be encoded into the chromosomes and as such, trimming away sections of infeasible regions in constraint optimization problems. This results in a smaller search space and reduces the efforts of evolution in finding the global optimum solution. In addition, the proposed constraint handling method can be incorporated in many objective domain based methods to remove some of the infeasible regions before applying these methods and are compatible with standard genetic operators like crossover and mutation without the need of rejecting/repairing any infeasible solutions as adopted in most existing methods.|
|Source Title:||Proceedings of the IEEE Conference on Evolutionary Computation, ICEC|
|Appears in Collections:||Staff Publications|
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