Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Statistical Characteristics of Evolution Strategies
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Efficiency and Mutation Strength Adaptation of the (mu, muI, lambda)-ES in a Noisy Environment
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Random Dynamics Optimum Tracking with Evolution Strategies
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
On the Benefits of Random Memorizing in Local Evolutionary Search
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Toward a theory of evolution strategies: The (μ, λ)-theory
Evolutionary Computation
Toward a theory of evolution strategies: On the benefits of sex---the (μ/μ, λ) theory
Evolutionary Computation
Toward a theory of evolution strategies: Self-adaptation
Evolutionary Computation
Some comments on evolutionary algorithm theory
Evolutionary Computation
Rigorous hitting times for binary mutations
Evolutionary Computation
On the scalability of parallel genetic algorithms
Evolutionary Computation
Optimal sampling of genetic algorithms on polynomial regression
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Differential Evolution with Noise Analyzer
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
On the limitations of adaptive resampling in using the student's t-test evolution strategies
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
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
Selection in the presence of noise
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
A differential evolution for optimisation in noisy environment
International Journal of Bio-Inspired Computation
Exploiting overlap when searching for robust optima
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Integrating techniques from statistical ranking into evolutionary algorithms
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Finite Markov Chain Results in Evolutionary Computation: A Tour d'Horizon
Fundamenta Informaticae
Quantum control experiments as a testbed for evolutionary multi-objective algorithms
Genetic Programming and Evolvable Machines
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A method for the determination of the progress rate and the probability of success for the Evolution Strategy (ES) is presented. The new method is based on the asymptotical behavior of the χ-distribution and yields exact results in the case of infinite-dimensional parameter spaces. The technique is demonstrated for the (l,+ λ) ES using a spherical model including noisy quality functions. The results are used to discuss the convergence behavior of the ES.