Reaearch On Biologically-Inspired Algorithms To Complex Optimixation Problems

Genetic algorithms and neural network are new biologically-inspiredoptimization methods. These algorithms are based on certain concepts of biologicalevolution and operation of the brain. In this paper some new genetic algorithmsbased on experimental design are proposed. Besides, an improved Hybrid LT schemeis presented and its convergence is discussed.This paper starts with a basic structure of GAs and broad survey of currenttechniques used in GAs. Chapter 2 includes a brief introduction of uniform design andorthogonal design, and discusses how to incorporate these experimental designs intoevolutionary operators, which is the foundation of the resulting algorithms. Thenseveral new genetic algorithms based on the experimental design are proposed. Thesealgorithms includes algorithms for combinatorial optimization, multi-objectiveoptimization and nonlinear optimization, and their convergences are studied.Moreover, two concepts: square- deviation and entropy of nondominated set areproposed. They can quantitatively measure the quality of the nondominated setproduced from different algorithms, In order to promote the efficiency of nonlinearGAs, a new selection is presented. This paper also gives an improved Hybrid LTscheme, and its convergence is analyzed. At last, numerical experiments wereexecuted for these algorithms and the results show the effectiveness of thesealgorithms.