A theoretical basis

1. nonlinear programming

1. Nonlinear Programming
Extremes function of n a problem under the constraint of a set of equations or inequality, and in trust region method, quasi-Newton method sparse, parallel computing, research interior point method and the finite storage method.
2. Nonlinear Programming function
matlab in fmincon
substantially Usage : x = fmincon (fun, x0 , A, b, Aeq, beq, lb, ub); wherein,
Fun is a function defined by M file f (x), representative of the (non) linear objective function; X0 is the initial value of x;
a, B, Aeq, BEQ defines the linear constraint, if no linear constraint is a = [], b = [ ], Aeq = [], beq = [];
lb and ub are lower and upper bounds of the variables x, if there is no constraint lower and upper bounds, then lb = [], ub = [ ], can be written as the components are -INF lb, ub of each component They are inf.
3. The advantages and disadvantages of
classical nonlinear programming algorithm they use a gradient descent method for solving, strong local search capability, but the global search capability is weak.

2. The idea of genetic algorithms

1. Introduction
survival of the fittest "is a great law of nature, survival of the fittest, as the name implies, the genetic algorithm can be used to solve the problem of optimal genetic algorithm is a survival of the fittest, the final choice of the best groups. Ideal for handling traditional search complex and nonlinear optimization algorithm problem difficult to solve. At present, genetic algorithm is widely used in combinatorial optimization, machine learning, the field of signal processing, adaptive control and artificial life, etc.
2. the core idea
is to biological populations of a number of individuals were selection, crossover and mutation genetic operations, finally get optimal or near optimal solution during genetic manipulation, several important parameters:. L, M population size, crossover probability Pc, the length of chromosome mutation probability Pm, termination of algebraic T.
3. advantages and disadvantages of
strong global search ability, but weak local search, second-best solution to the problem can only get a general, rather than the optimal solution.