Introduction

We concerns global optimization (GO) problems, in which one wants to get the globally best solution of an objective function f(x) on a given domain, where f might be multi-modal, non-differentiable, discontinuous, or even worse black-box type.      Difficulties:     1. Derivative-based methods are easily get trapped into local optimum.     2. Derivative of objective function is unavailable, or hard tocompute, or         unreliable (numerically unstable, e.g., in the presence of noise).             So derivative-free methods, including evolutionary algorithms, are preferred.       Some efficient evolutionary algorithms for GO:     1. Real-coded genetic algorithm (RGA)     2. Genetic programming (GP)     3. Particle swarm optimization (PSO)     4. Differential evolution (DE)     5. Low dimensional simplex evolution (LDSE) To discuss with the colleagues who work in global optimization field,  please visit Google group on global optimization.