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Yes. The ruggedness of a landscape is a measure of how much variability is observed between neighbouring solutions, and it can be computed using the landscape correlation function. Rugged landscapes (with a very low correlation) typically have lots of local minima and are more difficult to traverse than smooth landscapes (correlation close to 1). For a fixed ...


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This talk discusses several approaches to integrate machine learning in local search algorithms by identifying good solutions bad solutions promising neighborhoods through offline learning of problem instance and solution features. This paper looks at the frequency of good (partial) paths of locally optimal solutions during runtime. Good partial solutions ...


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Here (4) refers to a set of constraints, written for all possible paths with exactly $K$ vertices. The formulation is therefore non-compact which means the no. of these constraints grow exponentially as the problem size (no. of nodes) increases. In this case, there would be $\frac1n\cdot {}^nP_r$ constraints, where $n = |V|$, $r = K$ and $P$ represents ...


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In pseudocode, you'd have to do the following: Create a binary $x_{ij}$ variable for every arc $(i,j)\in A$. Use the boolVar(.) method in the IloCplexModeler class. Personally I find it convenient to create a Map that maps an edge to its variable to quickly lookup the variable that corresponds to a given arc. If you use Java, you would have to define an Arc ...


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