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
Genetic Local Search Algorithms for the Travelling Salesman Problem
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
On the Analysis of Evolutionary Algorithms - A Proof That Crossover Really Can Help
ESA '99 Proceedings of the 7th Annual European Symposium on Algorithms
When a genetic algorithm outperforms hill-climbing
Theoretical Computer Science
On the Optimization of Monotone Polynomials by Simple Randomized Search Heuristics
Combinatorics, Probability and Computing
On the local performance of simulated annealing and the (1+1) evolutionary algorithm
Proceedings of the 8th annual conference on Genetic and evolutionary computation
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
Simulated annealing beats metropolis in combinatorial optimization
ICALP'05 Proceedings of the 32nd international conference on Automata, Languages and Programming
IEEE Transactions on Evolutionary Computation
Genetic Algorithms and Very Fast Simulated Reannealing: A comparison
Mathematical and Computer Modelling: An International Journal
Memetic algorithms with variable-depth search to overcome local optima
Proceedings of the 10th annual conference on Genetic and evolutionary computation
On the utility of the population size for inversely fitness proportional mutation rates
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
Hybrid wireless Network on Chip: a new paradigm in multi-core design
Proceedings of the 2nd International Workshop on Network on Chip Architectures
Free lunches on the discrete Lipschitz class
Theoretical Computer Science
Analysis of (1+1) evolutionary algorithm and randomized local search with memory
Evolutionary Computation
Performance evaluation and design trade-offs for wireless network-on-chip architectures
ACM Journal on Emerging Technologies in Computing Systems (JETC)
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The development in the area of randomized search heuristics has shown the importance of a rigorous theoretical analysis of the performance of these heuristics. Unfortunately, the analysis of the expected optimization time of a specific algorithm has in general no implications on the behaviour of other algorithms - even if they differ only in some aspects. Indeed, small differences may imply large differences in the optimization time. Hence, it is an important issue to compare fundamental heuristics and to find out for which problems they behave in such a similar way that results on one heuristic can be transferred to the other one and to describe problems where they behave quite differently. Such an approach is performed here to the simple and well-known (1+1) EA, which is based on elitist selection and a global search operator, and simulated annealing, which is based on nonelitist selection and a local search operator.