Experimental results on the crossover point in random 3-SAT
Artificial Intelligence - Special volume on frontiers in problem solving: phase transitions and complexity
Phase Transitions and Backbones of 3-SAT and Maximum 3-SAT
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
Stochastic search and phase transitions: AI meets physics
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Improving Performance in Combinatorial Optimisation Using Averaging and Clustering
EvoCOP '09 Proceedings of the 9th European Conference on Evolutionary Computation in Combinatorial Optimization
Learning the large-scale structure of the MAX-SAT landscape using populations
IEEE Transactions on Evolutionary Computation
Diagnosing multiple intermittent failures using maximum likelihood estimation
Artificial Intelligence
| Hi-index | 0.00 |
Hill-climbing has been shown to be more effective than exhaustive search in solving satisfiability problems. Also, it has been used either by itself or in combination with other methods to solve the most difficult region of SAT, the phase transition. We show that hill-climbing also finds SAT problems difficult around the phase transition. It too follows an easy-hard-eays transition.