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1.A simple and efficient global optimization method, Accelerated Controlled Random Search (ACRS), is presented in this paper.
2.Genetic algorithm is a random search algorithm that simulates natural selection and evolution. It searches through the total solution space and can find the optimal solution globally over a domain.
3.The first method is based on the artificial potential field. And the second one is suggested by Rapidly-exploring Random Tree (RRT) and rolling windows planning.
4.Using NLJ search procedure to optimize the control loops of FCCU plant by selecting PID parameters based on IMC control principle and step response data.
5.Using the accelerated controlled random search (ACRS) to perform the canonical piecewise-linear modeling, this method overcomes the difficulties of multiminimum in the modeling.
6.A genetic algorithm (GA) based on building block recognition was proposed, in which building block candidates were recognized in evolving process to speed up the search so as to avoid the blindness of GA random searching.
7.Genetic Algorithms (GA) is a high-effective randomly searching algorithms, which bases on the natural evolution and Darwinism. It is a very effective algorithm to resolve NP-Completed combination optimization problem.
8.The purpose of the essay is to work out a new feasible way to solve this very problem of Nonlinear Combination Forecasting. The research of AIA began from the late 80’s in 20th century. It gained from imitating the biological immune system.
9.Considering the one-sidedness and inaccuracy of knowledge discovery only from single-color database,an approach is proposed to discover knowledge from 1331 groups of mix-color database with partial least-square regression,based on measuring and learning 400 groups of single-color database.By this method,the mean error decreases when converting from RGB to CMYK,the precision of color matching is improved,and the automatic and general problem in color matching is further solved.
10.A Stochastic Heuristic Search Method for Extracting Edge in Noise Image
