title: Hybrid evolutionary programming for heavily constrained problems.
abstract: A hybrid of evolutionary programming (EP) and a deterministic optimization procedure is applied to a series of non-linear and quadratic optimization problems. The hybrid scheme is compared with other existing schemes such as EP alone, two-phase (TP) optimization, and EP with a non-stationary penalty function (NS-EP). The results indicate that the hybrid method can outperform the other methods when addressing heavily constrained optimization problems in terms of computational efficiency and solution accuracy.
authors.affiliation: Department of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST) Kusung-dong, Taejon-shi, Republic of Korea.
authors.forename: H
authors.initials: H
authors.lastname: Myung
authors.forename: J H
authors.initials: JH
authors.lastname: Kim
mesh_terms: D005075:Biological Evolution; D003198:Computer Simulation; D008433:Mathematics; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D012984:Software