Speeding up evolutionary algorithms through restricted mutation operators
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Computational complexity and evolutionary computation
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Analysis of a simple evolutionary algorithm for the multiobjective shortest path problem
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
Evolutionary algorithms and dynamic programming
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Computational complexity and evolutionary computation
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Analysis of an asymmetric mutation operator
Evolutionary Computation
Computational complexity and evolutionary computation
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Drift analysis with tail bounds
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Exploring the runtime of an evolutionary algorithm for the multi-objective shortest path problem**
Evolutionary Computation
Evolutionary algorithms and dynamic programming
Theoretical Computer Science
Tight analysis of the (1+1)-ea for the single source shortest path problem
Evolutionary Computation
Revisiting the restricted growth function genetic algorithm for grouping problems
Evolutionary Computation
Analysis of speedups in parallel evolutionary algorithms for combinatorial optimization
ISAAC'11 Proceedings of the 22nd international conference on Algorithms and Computation
Approximating vertex cover using edge-based representations
Proceedings of the twelfth workshop on Foundations of genetic algorithms XII
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We propose and analyze a novel genotype to represent walk and cycle covers in graphs, namely matchings in the adjacency lists. This representation admits the natural mutation operator of adding a random match and possibly also matching the former partners. To demonstrate the strength of this set-up, we use it to build a simple (1+1) evolutionary algorithm for the problem of finding an Eulerian cycle in a graph. We analyze several natural variants that stem from different ways to randomly choose the new match. Among other insight, we exhibit a (1+1) evolutionary algorithm that computes an Euler tour in a graph with $m$ edges in expected optimization time Θ(m log m). This significantly improves the previous best evolutionary solution having expected optimization time Θ(m2 log m) in the worst-case, but also compares nicely with the runtime of an optimal classical algorithm which is of order Θ(m). A simple coupon collector argument indicates that our optimization time is asymptotically optimal for any randomized search heuristic.