Real options approach to evaluating genetic algorithms
Applied Soft Computing
When is an estimation of distribution algorithm better than an evolutionary algorithm?
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Rigorous time complexity analysis of univariate marginal distribution algorithm with margins
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Runtime analysis of a binary particle swarm optimizer
Theoretical Computer Science
Analysis of computational time of simple estimation of distribution algorithms
IEEE Transactions on Evolutionary Computation
A few ants are enough: ACO with iteration-best update
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Simple max-min ant systems and the optimization of linear pseudo-boolean functions
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
On the optimal convergence probability of univariate estimation of distribution algorithms
Evolutionary Computation
Evolutionary algorithm characterization in real parameter optimization problems
Applied Soft Computing
How the (1+λ) evolutionary algorithm optimizes linear functions
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Runtime analysis of the (1+1) EA on computing unique input output sequences
Information Sciences: an International Journal
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Estimation of distribution algorithms (EDAs) solve an optimization problem heuristically by finding a probability distribution focused around its optima. Starting with the uniform distribution, points are sampled with respect to this distribution and the distribution is changed according to the function values of the sampled points. Although there are many successful experiments suggesting the usefulness of EDAs, there are only few rigorous theoretical results apart from convergence results without time bounds. Here we present first rigorous runtime analyses of a simple EDA, the compact genetic algorithm (cGA), for linear pseudo-Boolean functions on n variables. We prove a general lower bound for all functions and a general upper bound for all linear functions. Simple test functions show that not all linear functions are optimized in the same runtime by the cGA.