Journal of Computer and System Sciences - 26th IEEE Conference on Foundations of Computer Science, October 21-23, 1985
Cooling schedules for optimal annealing
Mathematics of Operations Research
Integrating and accelerating tabu search, simulated annealing, and genetic algorithms
Annals of Operations Research - Special issue on Tabu search
On the run-time behaviour of stochastic local search algorithms for SAT
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
On the convergence of generalized hill climbing algorithms
Discrete Applied Mathematics
Tabu Search
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Introduction to Algorithms
Efficiency of Local Search with Multiple Local Optima
SIAM Journal on Discrete Mathematics
Simulated Annealing: Searching for an Optimal Temperature Schedule
SIAM Journal on Optimization
SIAM Journal on Optimization
Local Search Algorithms for SAT: An Empirical Evaluation
Journal of Automated Reasoning
A class of convergent generalized hill climbing algorithms
Applied Mathematics and Computation
Studying the Complexity of Global Verification for NP-Hard Discrete Optimization Problems
Journal of Global Optimization
Global Optimization Performance Measures for Generalized Hill Climbing Algorithms
Journal of Global Optimization
Analyzing the performance of simultaneous generalized hill climbing algorithms
Computational Optimization and Applications
INFORMS Journal on Computing
A framework for analyzing sub-optimal performance of local search algorithms
Computational Optimization and Applications
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Generalized hill climbing algorithms provide a framework to describe and analyze metaheuristics for addressing intractable discrete optimization problems. The performance of such algorithms can be assessed asymptotically, either through convergence results or by comparison to other algorithms. This paper presents necessary and sufficient convergence conditions for generalized hill climbing algorithms. These conditions are shown to be equivalent to necessary and sufficient convergence conditions for simulated annealing when the generalized hill climbing algorithm is restricted to simulated annealing. Performance measures are also introduced that permit generalized hill climbing algorithms to be compared using random restart local search. These results identify a solution landscape parameter based on the basins of attraction for local optima that determines whether simulated annealing or random restart local search is more effective in visiting a global optimum. The implications and limitations of these results are discussed.