The time complexity of maximum matching by simulated annealing
Journal of the ACM (JACM)
The effect of the density of states on the Metropolis algorithm
Information Processing Letters
The metropolis algorithm for graph bisection
Discrete Applied Mathematics
Fitness Landscapes Based on Sorting and Shortest Paths Problems
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Evolutionary Algorithms and the Maximum Matching Problem
STACS '03 Proceedings of the 20th Annual Symposium on Theoretical Aspects of Computer Science
Journal of Computer Science and Technology
Simulated annealing beats metropolis in combinatorial optimization
ICALP'05 Proceedings of the 32nd international conference on Automata, Languages and Programming
Worst-case and average-case approximations by simple randomized search heuristics
STACS'05 Proceedings of the 22nd annual conference on Theoretical Aspects of Computer Science
Analysis of evolutionary algorithms for the longest common subsequence problem
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Evolutionary algorithms and matroid optimization problems
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Simulated annealing, its parameter settings and the longest common subsequence problem
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Analyses of simple hybrid algorithms for the vertex cover problem*
Evolutionary Computation
Exact Solutions to the Traveling Salesperson Problem by a Population-Based Evolutionary Algorithm
EvoCOP '09 Proceedings of the 9th European Conference on Evolutionary Computation in Combinatorial Optimization
A runtime analysis of simple hyper-heuristics: to mix or not to mix operators
Proceedings of the twelfth workshop on Foundations of genetic algorithms XII
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To understand the working principles of randomized search heuristics like evolutionary algorithms they are analyzed on optimization problems whose structure is well-studied. The idea is to investigate when it is possible to simulate clever optimization techniques for combinatorial optimization problems by random search. The maximum matching problem is well suited for this approach since long augmenting paths do not allow immediate improvements by local changes. It is known that randomized search heuristics like simulated annealing, the Metropolis algorithm, the (1+1) EA and randomized local search efficiently approximate maximum matchings for any graph; however, there are graphs where they fail to find maximum matchings in polynomial time. In this paper, we examine randomized local search (RLS) for graphs whose structure is simple. We show that RLS finds maximum matchings on trees in expected polynomial time.