Expected Length of the Longest Probe Sequence in Hash Code Searching
Journal of the ACM (JACM)
Drift analysis and average time complexity of evolutionary algorithms
Artificial Intelligence
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
A study of drift analysis for estimating computation time of evolutionary algorithms
Natural Computing: an international journal
On the Choice of the Offspring Population Size in Evolutionary Algorithms
Evolutionary Computation
Runtime Analysis of the (μ+1) EA on Simple Pseudo-Boolean Functions
Evolutionary Computation
A rigorous analysis of the compact genetic algorithm for linear functions
Natural Computing: an international journal
Randomized local search, evolutionary algorithms, and the minimum spanning tree problem
Theoretical Computer Science
Algorithmic analysis of a basic evolutionary algorithm for continuous optimization
Theoretical Computer Science
A Blend of Markov-Chain and Drift Analysis
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
On the choice of the parent population size*
Evolutionary Computation
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Quasirandom evolutionary algorithms
Proceedings of the 12th annual conference on Genetic and evolutionary computation
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Drift analysis with tail bounds
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity
Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity
Algorithmica - Special Issue: Theory of Evolutionary Computation
On the analysis of the immune-inspired B-cell algorithm for the vertex cover problem
ICARIS'11 Proceedings of the 10th international conference on Artificial immune systems
On the analysis of the simple genetic algorithm
Proceedings of the 14th annual conference on Genetic and evolutionary computation
The choice of the offspring population size in the (1,λ) EA
Proceedings of the 14th annual conference on Genetic and evolutionary computation
| Hi-index | 0.00 |
We analyze how the (1+λ) evolutionary algorithm (EA) optimizes linear pseudo-Boolean functions. We prove that it finds the optimum of any linear function within an expected number of O(1/λn log n+n) iterations. We also show that this bound is sharp for some functions, e.g., the binary value function. Hence unlike for the(1+1) EA, for the (1+λ) EA different linear functions may have run-times of different asymptotic order. The proof of our upper bound heavily relies on a number of classic and recent drift analysis methods. In particular, we show how to analyze a process displaying different types of drifts in different phases. Our work corrects a wrongfully claimed better asymptotic runtime in an earlier work~\cite{He10}.