Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Cooling schedules for optimal annealing
Mathematics of Operations Research
Using controlled experiments in layout
ACM SIGDA Newsletter
Towards an analysis of local optimization algorithms
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Maximum Matching on Boltzmann Machines
Neural Processing Letters
Approximating discrete collections via local improvements
Proceedings of the sixth annual ACM-SIAM symposium on Discrete algorithms
Hill-climbing finds random planted bisections
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Evolutionary Algorithms and the Maximum Matching Problem
STACS '03 Proceedings of the 20th Annual Symposium on Theoretical Aspects of Computer Science
Simulated Annealing and Graph Colouring
Combinatorics, Probability and Computing
ON THE CONVERGENCE OF METROPOLIS-TYPE RELAXATION AND ANNEALING WITH CONSTRAINTS
Probability in the Engineering and Informational Sciences
A study of drift analysis for estimating computation time of evolutionary algorithms
Natural Computing: an international journal
Journal of Computer Science and Technology
Computers and Operations Research
On the Optimization of Monotone Polynomials by Simple Randomized Search Heuristics
Combinatorics, Probability and Computing
Some theoretical results about the computation time of evolutionary algorithms
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Maximum cardinality matchings on trees by randomized local search
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Randomized local search, evolutionary algorithms, and the minimum spanning tree problem
Theoretical Computer Science
About the Time Complexity of Evolutionary Algorithms Based on Finite Search Space
Computational Intelligence and Security
Analysis of the (1 + 1)-EA for finding approximate solutions to vertex cover problems
IEEE Transactions on Evolutionary Computation
On the optimization of monotone polynomials by the (1+1) EA and randomized local search
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Necessary and sufficient conditions for success of the metropolis algorithm for optimization
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Theoretical results in genetic programming: the next ten years?
Genetic Programming and Evolvable Machines
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Simulated annealing beats metropolis in combinatorial optimization
ICALP'05 Proceedings of the 32nd international conference on Automata, Languages and Programming
NetDEO: automating network design, evolution, and optimization
Proceedings of the 2012 IEEE 20th International Workshop on Quality of Service
Runtime analysis of evolutionary algorithms: basic introduction
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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The random, heuristic search algorithm called simulated annealing is considered for the problem of finding the maximum cardinality matching in a graph. It is shown that neither a basic form of the algorithm, nor any other algorithm in a fairly large related class of algorithms, can find maximum cardinality matchings such that the average time required grows as a polynomial in the number of nodes of the graph. In contrast, it is also shown for arbitrary graphs that a degenerate form of the basic annealing algorithm (obtained by letting “temperature” be a suitably chosen constant) produces matchings with nearly maximum cardinality in polynomial average time.