The area of approximation algorithms is aimed at giving provable guarantees on the performance of heuristics for hard problems. The course will present general techniques (such as convex ...

Abstract: In this paper, we present some initial results of several meta-heuristic optimization algorithms, namely, genetic algorithms, simulated annealing, branch and bound, dynamic programming, ...

Abstract: Knapsack problem is a classical optimization problem in computer science and programming. Knapsack problem main objective is to solve how much the maximum profit can be carried with the ...

The question A is about the Peano curve, which is the advanced level of Hilbert curve, quite interesting. The question B is the application of segment Tree, which is ...

A major barrier to the wider use of supervised learning in emerging applications, such as genomic selection, is the lack of sufficient and representative labeled data to train prediction models. The ...

The best algorithm for a computational problem generally depends on the “relevant inputs,” a concept that depends on the application domain and often defies formal articulation. Although there is a ...

[Ahuja00] “A greedy genetic algorithm for the quadratic assignment problem”, R. Ahuja, J. Orlin, A. Tiwari, Computers and Operations Research, vol. 27, issue 10 (Sept. 2000), 917--934, ACM (2000) ...