Using dual approximation algorithms for scheduling problems theoretical and practical results
Journal of the ACM (JACM)
The rate of convergence to optimality of the LPT rule
Discrete Applied Mathematics
Randomized algorithms
Algorithms for Scheduling Independent Tasks
Journal of the ACM (JACM)
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Evolutionary Algorithms and the Maximum Matching Problem
STACS '03 Proceedings of the 20th Annual Symposium on Theoretical Aspects of Computer Science
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
Runtime analysis of the (1+1) evolutionary algorithm on strings over finite alphabets
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
Proceedings of the 14th annual conference on Genetic and evolutionary computation
A parameterized runtime analysis of simple evolutionary algorithms for makespan scheduling
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
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Two of the major difficulties dealing with real-world problems nowadays are their increasing complexity and the decreasing available timespan to create "acceptable" solutions. Due to this and the strongly decreasing costs of CPU-power, non specialized (random) search heuristics gain more and more importance. In this paper we analyze the behavior of two very simple search heuristics on a strongly NP-hard scheduling problem. Although both find feasible solutions in pseudo-polynomial time, at least one of them is not able to present an (1+ε)-approximation for arbitrary ε0 with constant probability. Despite this, one of the two presented search heuristics can even compete with a problem-specific algorithm on a certain class of inputs and deliver solutions convergent to optimality for increasing problem size.