Artificial evolution and artificial intelligence
Machine Learning: Principles and techniques
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Predictive models for the breeder genetic algorithm i. continuous parameter optimization
Evolutionary Computation
Toward a theory of evolution strategies: Some asymptotical results from the (1,+ λ)-theory
Evolutionary Computation
Toward a theory of evolution strategies: On the benefits of sex---the (μ/μ, λ) theory
Evolutionary Computation
How to analyse evolutionary algorithms
Theoretical Computer Science - Natural computing
Statistical Characteristics of Evolution Strategies
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
On the benefits of populations for noisy optimization
Evolutionary Computation
Analysis of the (1, λ) - ES on the Parabolic Ridge
Evolutionary Computation
Analysis of the (μ/μ, λ) - ES on the Parabolic Ridge
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
Theory of Evolutionary Algorithm: A View from Thermodynamics
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007
Finite Markov Chain Results in Evolutionary Computation: A Tour d'Horizon
Fundamenta Informaticae
On the equivalences and differences of evolutionary algorithms
Engineering Applications of Artificial Intelligence
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The multimembered evolution strategy (ES) acting on μ parents and λ offspring is analyzed for real-valued, N-dimensional parameter spaces (N 30). N-dependent progress rate formulas are derived for (1, λ) and (μ, λ) strategies on spherical models. The analytical results obtained are compared with simulation experiments for the (hyper)sphere and the inclined (hyper)plane.