Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Hill-climbing finds random planted bisections
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
Introduction to Algorithms
On the Choice of the Mutation Probability for the (1+1) EA
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Genetic Local Search Algorithms for the Travelling Salesman Problem
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
When a genetic algorithm outperforms hill-climbing
Theoretical Computer Science
Rigorous hitting times for binary mutations
Evolutionary Computation
Simulated annealing for graph bisection
SFCS '93 Proceedings of the 1993 IEEE 34th Annual Foundations of Computer Science
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
On the local performance of simulated annealing and the (1+1) evolutionary algorithm
Proceedings of the 8th annual conference on Genetic and evolutionary computation
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Evolutionary algorithms belong to the class of general randomized search heuristics. Theoretical investigations often concentrate on simple instances like the well-known (1+1) EA. This EA is very similar to simulated annealing, another general randomized search heuristic. These two algorithms are systematically compared under the perspective of the expected optimization time when optimizing pseudo-boolean functions. It is investigated how well the algorithmic similarities can be exploited to transfer analytical results from one algorithm to the other. Limitations of such an approach are illustrated by the presentation of example functions where the performance of the two algorithms differs in an extreme way. Furthermore, an attempt is made to characterize classes of functions where such a transfer of results is more successful.