Randomized algorithms
Future Generation Computer Systems
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
Ant Colony Optimization
On the Optimization of Monotone Polynomials by Simple Randomized Search Heuristics
Combinatorics, Probability and Computing
Probability and Computing: Randomized Algorithms and Probabilistic Analysis
Probability and Computing: Randomized Algorithms and Probabilistic Analysis
Ant colony optimization theory: a survey
Theoretical Computer Science
On the runtime analysis of the 1-ANT ACO algorithm
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Rigorous hitting times for binary mutations
Evolutionary Computation
First steps to the runtime complexity analysis of ant colony optimization
Computers and Operations Research
Runtime analysis of a simple ant colony optimization algorithm
ISAAC'06 Proceedings of the 17th international conference on Algorithms and Computation
IEEE Transactions on Evolutionary Computation
Runtime analysis of binary PSO
Proceedings of the 10th annual conference on Genetic and evolutionary computation
How Single Ant ACO Systems Optimize Pseudo-Boolean Functions
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Rigorous Analyses for the Combination of Ant Colony Optimization and Local Search
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
Ant Colony Optimization and the Minimum Spanning Tree Problem
Learning and Intelligent Optimization
Runtime analysis of an ant colony optimization algorithm for TSP instances
IEEE Transactions on Evolutionary Computation
When to use bit-wise neutrality
Natural Computing: an international journal
Ant Colony Optimization and the minimum spanning tree problem
Theoretical Computer Science
Using markov-chain mixing time estimates for the analysis of ant colony optimization
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
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Recently, the first rigorous runtime analyses of ACO algorithms have been presented. These results concentrate on variants of the MAX-MIN ant system by Stützle and Hoos and consider their runtime on simple pseudo-Boolean functions such as OneMax and LeadingOnes. Interestingly, it turns out that a variant called 1-ANT is very sensitive to the choice of the evaporation factor while a recent technical report by Gutjahr and Sebastiani suggests partly opposite results for their variant called MMAS. In this paper, we elaborate on the differences between the two ACO algorithms, generalize the techniques by Gutjahr and Sebastiani and show improved results.