Ruggedness Quantifying for Constrained Continuous Fitness Landscapes

Constrained optimization problems appear frequently in important real-world applications. In this chapter, we study algorithms for constrained optimiation problems from a theoretical perspective. Our goal is to understand how the fitness landscape influences the success of certain types of algorithms. One important feature for analyzing and classifying fitness landscape is its ruggedness. It is generally assumed that rugged landscapes make the optimization process by bio-inspired computing methods much harder than smoothed landscapes, which give clear hints toward an optimal solution. We introduce different methods for quantifying the ruggedness of a given constrained optimization problem. They, in particular, take into account how to deal with infeasible regions in the underlying search space.

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