Abstract
Many stochastic search algorithms have recently been developed to make more remarkable progress than systematic search algorithms because stochastic algorithms sometimes solve large-scale constraint satisfaction problems in a practical time. However, such stochastic algorithms have the drawback of getting stuck in local optima which are not acceptable as final solutions. We analyze an iterative improvement algorithm from the viewpoint of constraint structures causing local optima. Using the graph-coloring problem with three colors, an archetype problem to evaluate constraint satisfaction algorithms, we study the local graph structures around which so many conflicts thrash. We propose a key constraint structure, an LM pair, which may induce a local optimum, and clarify the mechanism of how conflicting colors for an LM pair obstruct the stepwise refinement of hill-climbing. Experimental results show that LM pairs are strongly correlated with the search efficiency of the stochastic search algorithm.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Transactions of the Japanese Society for Artificial Intelligence
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
