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Abstract: This research investigates the use of intelligent techniques for the bipartite drawing problem (BDP). Due to the combinatorial nature of the solution space, the use of traditional search methods lead to exponential time. Hence, the aim of this paper is to speed up the search time when solving the BDP through the use of Evolutionary Algorithms (EAs) and Barycenter Heuristic (BC). EA is applied on the BDP wherein genetic operators such as crossover and mutation are employed while searching for the best possible solution. The results show that the EA approach guides the search towards optimal solutions and in many instances it outperforms the BC. Key words: Evolutionary algorithm, edge crossing, bipartite graph, barycenter heuristic, NP-complete problems

Experimental evaluation of SAT algorithms has been practiced and debated with considerable intensity over the last decade. Typical comparisons involve a collection of unrelated benchmark instances. Algorithms are evaluated on the basis of their total or average execution time for the collection. We propose a significant departure from this approach. By introducing equivalence classes of closely related instances for each reference formula, we can experimentally deduce the 95% confidence interval of the mean time-to-solve and other significantly correlated metrics. Instances derived from the reference formula that are perceived `hard' by the algorithm under test may exhibit max/min ratios of a metric that can range anywhere from 2 to 1000 and beyond. In such cases, comparisons based on single formulas have no statistical merit. A total of four class types are formalized and experiments are performed on at least 32 instances in each class. We introduce an experimental design environment and present experimental results that reveal startling, and statistically significant, di#erentiations between three state-of-the art algorithms and a vanilla DPLL algorithm. As a side benefit, these results provide a number of insights to improve each of these algorithms. 1.