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Center for Nonlinear Studies |
During the last few decades, Evolutionary Computing (EC) has emerged as a powerful methodology for tackling the often highly complex problems of modern society, such as optimizing engineering design, job shop scheduling, and transport systems. Such real-world optimization problems typically are characterized by extremely large, ill-behaved solution spaces which are infeasible to exhaustively search and defy traditional optimization algorithms because they are for instance non-linear, non-differentiable, non-continuous, or non-convex. EC encompasses a class of stochastic, population-based, optimization algorithms inspired by natural evolution theory and genetics which have been shown to perform well on many problems with large, ill-behaved solution spaces. This talk will discuss current grand challenges in EC and describe several active research projects being carried out at Missouri S&T’s Natural Computation Laboratory which aim to address some of those grand challenges.
Host: Alexander Kent