Theory of evolutionary algorithms: a bird's eye view
Theoretical Computer Science - Special issue on evolutionary computation
The theory of evolution strategies
The theory of evolution strategies
Evolution strategies in noisy environments- a survey of existing work
Theoretical aspects of evolutionary computing
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Genetic Algorithms in Noisy Environments
Machine Learning
Where Elitists Start Limping Evolution Strategies at Ridge Functions
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Evolution Strategies on Noisy Functions: How to Improve Convergence Properties
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Optimization with Noisy Function Evaluations
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Toward a theory of evolution strategies: On the benefits of sex---the (μ/μ, λ) theory
Evolutionary Computation
Genetic algorithms, selection schemes, and the varying effects of noise
Evolutionary Computation
Local performance of the (1 + 1)-ES in a noisy environment
IEEE Transactions on Evolutionary Computation
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
A Comparison of Evolution Strategies with Other Direct Search Methods in the Presence of Noise
Computational Optimization and Applications
How to analyse evolutionary algorithms
Theoretical Computer Science - Natural computing
Qualms regarding the optimality of cumulative path length control in CSA/CMA-evolution strategies
Evolutionary Computation
Genetic Programming and Evolvable Machines
A New Approach for Predicting the Final Outcome of Evolution Strategy Optimization Under Noise
Genetic Programming and Evolvable Machines
Weighted multirecombination evolution strategies
Theoretical Computer Science - Foundations of genetic algorithms
Comparing evolutionary algorithms to the (1+1) -EA
Theoretical Computer Science
The steady state behavior of (µ/µI, λ)-ES on ellipsoidal fitness models disturbed by noise
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Optimal weighted recombination
FOGA'05 Proceedings of the 8th international conference on Foundations of Genetic Algorithms
Optimal computing budget allocation for small computing budgets
Proceedings of the Winter Simulation Conference
Hi-index | 5.23 |
The presence of noise in real-world optimization problems poses difficulties to optimization strategies. It is frequently observed that evolutionary algorithms are quite capable of succeeding in noisy environments. Intuitively, the use of a population of candidate solutions alongside with some implicit or explicit form of averaging inherent in the algorithms is considered responsible. However, so as to arrive at a deeper understanding of the reasons for the capabilities of evolutionary algorithms, mathematical analyses of their performance in select environments are necessary. Such analyses can reveal how the performance of the algorithms scales with parameters of the problem--such as the dimensionality of the search space or the noise strength--or of the algorithms--such as population size or mutation strength. Recommendations regarding the optimal sizing of such parameters can then be derived.The present paper derives an asymptotically exact approximation to the progress rate of the (µ/µI, λ)-evolution strategy (ES) on a finite-dimensional noisy sphere. It is shown that, in contrast to results obtained in the limit of infinite search space dimensionality, there is a finite optimal population size above which the efficiency of the strategy declines, and that therefore it is not possible to attain the efficiency that can be achieved in the absence of noise by increasing the population size. It is also shown that nonetheless, the benefits of genetic repair and an increased mutation strength make it possible for the multi-parent (µ/µI, λ)-ES to far outperform simple one-parent strategies.