A newly developed genetic hybrid algorithm (GHA) is applied for complex nonlinear programming problems. The algorithm combines features from parallel programming, classical nonlinear optimization methodology and evolutionary computation utilizing a powerful accelerator technique. The algorithm compares well with other evolutionary programming techniques on a set of difficult mathematical programming problems. The test results add significant evidence on the potential of the general solution framework in solving complicated optimization problems. Some suggestions for further research are also provided.
Not especially useful for representation information, but from p. 197 it lists a number of potentially useful test problems.
< RefMathiasWhitley1994 | ReferencesFound | RefSavikEtAl1995 >