Optimization Methods: Advanced Topics in Optimization - Evolutionary Algorithms for Optimization and Search

Most real world optimization problems involve complexities like discrete, continuous or mixed variables, multiple conflicting objectives, non-linearity, discontinuity and non-convex region. The search space (design space) may be so large that global optimum cannot be found in a reasonable time. The existing linear or nonlinear methods may not be efficient or computationally inexpensive for solving such problems. Various stochastic search methods like simulated annealing, evolutionary algorithms (EA) or hill climbing can be used in such situations. EAs have the advantage of being applicable to any combination of complexities (multi-objective, non-linearity etc) and also can be combined with any existing local search or other methods. Various techniques which make use of EA approach are Genetic Algorithms (GA), evolutionary programming, evolution strategy, learning classifier system etc. All these EA techniques operate mainly on a population search basis. In this lecture Genetic Algorithms, the most popular EA technique, is explained.