DM 63 - Heuristics for Combinatorial Optimization Weekly
نویسنده
چکیده
We discussed how to use tools from exploratory data analysis of statistics to visualize experimental results. We used the data collected from the Task 1 of the Implementation Contest. In the comparison there were three extra participants (ROS: Random Ordered Sequence greedy coloring, DSATUR, The degree of saturation and RLF, Recursive Large First). RLF and DSATUR clearly outperformed the other algorithms / implementations in solution quality. General aspects of designing, setting up and analyzing experiments were discussed. The R free software environment for statistical computing and graphics was recommended for performing the kind of analysis shown at the lecture. This kind of analysis are expected in the exam project. The complexity class PLS: Polynomial Local Search were introduced. Problems in PLS have the following properties: (1) local optimality can be verified in polynomial time and (2) improving steps can be computed in polynomial time. Finally the search landscape were introduced and some metaheuristics for escaping local optima were discussed.
منابع مشابه
DM 63 - Heuristics for Combinatorial Optimization Problems – Lecture
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