Tunably Rugged Landscapes With Known Maximum and Minimum PROJECT TITLE :Tunably Rugged Landscapes With Known Maximum and MinimumABSTRACT:We tend to propose NM landscapes as a brand new class of tunably rugged benchmark problems. NM landscapes are well defined on alphabets of any arity, including each discrete and real-valued alphabets, include epistasis in a natural and transparent manner, are proven to possess known value and site of the global maximum and, with some extra constraints, are proven to additionally have a known global minimum. Empirical studies are used to illustrate that, when coefficients are selected from a counseled distribution, the ruggedness of NM landscapes is smoothly tunable and correlates with many measures of search problem. We discuss why these properties create NM landscapes preferable to each NK landscapes and Walsh polynomials as benchmark landscape models with tunable epistasis. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Bayesian $M$ -Ary Hypothesis Testing: The Meta-Converse and Verdú-Han Bounds Are Tight Ship Classification Based on Superstructure Scattering Features in SAR Images
