This research project approaches the general nonlinear programming problem through the use of a hybrid systems based on genetic algorithms. The systems, in which genetic operators may vary with the type of problem, is designed to handel both continuous and discrete objective functions and both linear and non linear constraints on continuous, integer, and Boolean variables. The approach is experimental, based on the design, implementation, and evaluation of promising approaches, building on the investigator's previous work in genetic algorithms. An underlying objective of the project is to develop theoretical foundations for continuous genetic algorithms, and results of the research should also provide a better understanding of the principles of genetic algorithms applicability to constrained problems.
