Computers and Operations Research
The metropolis algorithm for graph bisection
Discrete Applied Mathematics
Introduction to Algorithms
Evolutionary Algorithms and the Maximum Matching Problem
STACS '03 Proceedings of the 20th Annual Symposium on Theoretical Aspects of Computer Science
ESA '94 Proceedings of the Second Annual European Symposium on Algorithms
Crossover is provably essential for the Ising model on trees
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Complexity Theory: Exploring the Limits of Efficient Algorithms
Complexity Theory: Exploring the Limits of Efficient Algorithms
Real royal road functions: where crossover provably is essential
Discrete Applied Mathematics - Special issue: Boolean and pseudo-boolean funtions
On the analysis of the (1+1) memetic algorithm
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Theoretical Computer Science
Theoretical analysis of diversity mechanisms for global exploration
Proceedings of the 10th annual conference on Genetic and evolutionary computation
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
Local search in evolutionary algorithms: the impact of the local search frequency
ISAAC'06 Proceedings of the 17th international conference on Algorithms and Computation
IEEE Transactions on Evolutionary Computation
Theoretical analysis of diversity mechanisms for global exploration
Proceedings of the 10th annual conference on Genetic and evolutionary computation
The impact of parametrization in memetic evolutionary algorithms
Theoretical Computer Science
Analysis of diversity-preserving mechanisms for global exploration*
Evolutionary Computation
Hybridizing evolutionary algorithms with opportunistic local search
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Variable-depth search (shortly VDS) is well-known as Lin-Kernighan strategy for the TSP and Kernighan-Lin for graph partitioning. The basic idea is to make a sequence of local moves and to freeze all moved combinatorial objects to prevent the search from looping. VDS stops when no further local move is possible and returns a best found solution. We analyze memetic algorithms with VDS for three binary combinatorial problems: Mincut, Knapsack, and Maxsat. More precisely, we focus on simply structured problem instances containing local optima that are very hard to overcome. Many common trajectory-based algorithms fail to find a global optimum: the (1+1)EA, iterated local search, and simulated annealing need exponential time with high probability. However, memetic algorithms using VDS easily manage to find a global optimum in expected polynomial time. These results strengthen the usefulness of memetic algorithms with VDS from a theoretical perspective.