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IEEE Transactions on Software Engineering
Protocol testing: review of methods and relevance for software testing
ISSTA '94 Proceedings of the 1994 ACM SIGSOFT international symposium on Software testing and analysis
Randomized algorithms
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
Switching and Finite Automata Theory: Computer Science Series
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Introduction to Algorithms
Testing Finite-State Machines: State Identification and Verification
IEEE Transactions on Computers
Theoretical Computer Science - Natural computing
Fitness Landscapes Based on Sorting and Shortest Paths Problems
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
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Natural Computing: an international journal
Real royal road functions for constant population size
Theoretical Computer Science
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
On the effect of populations in evolutionary multi-objective optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
A rigorous analysis of the compact genetic algorithm for linear functions
Natural Computing: an international journal
Automated Unique Input Output Sequence Generation for Conformance Testing of FSMs
The Computer Journal
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Software Testing, Verification & Reliability
Randomized local search, evolutionary algorithms, and the minimum spanning tree problem
Theoretical Computer Science
The Current State and Future of Search Based Software Engineering
FOSE '07 2007 Future of Software Engineering
Proceedings of the 2007 international symposium on Software testing and analysis
Rigorous analyses of simple diversity mechanisms
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Theoretical analysis of diversity mechanisms for global exploration
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Population size versus runtime of a simple evolutionary algorithm
Theoretical Computer Science
ICSTW '08 Proceedings of the 2008 IEEE International Conference on Software Testing Verification and Validation Workshop
On the impact of the mutation-selection balance on the runtime of evolutionary algorithms
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
Real royal road functions-where crossover provably is essential
Discrete Applied Mathematics - Special issue: Boolean and pseudo-boolean funtions
On the brittleness of evolutionary algorithms
FOGA'07 Proceedings of the 9th international conference on Foundations of genetic algorithms
Theoretical analysis of local search in software testing
SAGA'09 Proceedings of the 5th international conference on Stochastic algorithms: foundations and applications
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Finite Markov Chain Results in Evolutionary Computation: A Tour d'Horizon
Fundamenta Informaticae
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Computing unique input output (UIO) sequences is a fundamental and hard problem in conformance testing of finite state machines (FSM). Previous experimental research has shown that evolutionary algorithms (EAs) can be applied successfully to find UIOs for some FSMs. However, before EAs can be recommended as a practical technique for computing UIOs, it is necessary to better understand the potential and limitations of these algorithms on this problem. In particular, more research is needed in determining for what instance classes of the problem EAs are feasible, and for what instance classes EAs are provably better than random search strategies. This paper presents rigorous theoretical and numerical analyses of the runtime of the (1+1) EA and random search on several selected instance classes of this problem. The theoretical analysis shows firstly, that there are instance classes where the EA is efficient, while random testing fails completely. Secondly, an instance class that is difficult for both random testing and the EA is presented. Finally, a parametrised instance class with tunable difficulty is presented. The numerical study estimates the constants in the asymptotic expressions obtained in the theoretical analysis, and the variability of the runtime. The numerical results fit well with the theoretical results, even for small problem instance sizes. Together, these results provide a first theoretical characterisation of the potential and limitations of the (1+1) EA on the problem of computing UIOs.