Optimal Static and Self-Adjusting Parameter Choices for the $$(1+(\lambda ,\lambda ))$$ ( 1 + ( λ , λ ) ) Genetic Algorithm

Optimal Parameter Settings for the $(1+(\lambda, \lambda))$ Genetic Algorithm

The (1 + (λ, λ)) genetic algorithm is one of the few algorithms for which a super-constant speed-up through the use of crossover could be proven. So far, this algorithm has been used with parameters based also on intuitive considerations. In this work, we rigorously regard the whole parameter space and show that the asymptotic time complexity proven by Doerr and Doerr (GECCO 2015) for the intui...

The (1+$\lambda$) Evolutionary Algorithm with Self-Adjusting Mutation Rate

We propose a new way to self-adjust the mutation rate in population-based evolutionary algorithms in discrete search spaces. Roughly speaking, it consists of creating half the offspring with a mutation rate that is twice the current mutation rate and the other half with half the current rate. The mutation rate is then updated to the rate used in that subpopulation which contains the best offspr...

Self-Adjusting Parameter Choices for Discrete Black-Box Optimization

Double Λ Hypernuclei and the Λ-λ Interaction. *

We approximate the double Λ hypernuclei by systems composed by two interacting Λ’s moving in the mean field potential created by the nuclear cores. The Λ-core potentials are adjusted to reproduce the binding energies of the corresponding single Λ hypernuclei [1]. A σ-ω meson exchange potential [2] is used for the Λ-Λ interaction. We apply both the Hartree-Fock (HF) and variational approximation...

Ĝ Ĝ Λ 11 Λ 12 Λ 21 Λ 21 Λ 22 Λ 22 Λ Λ̃ 0 0 Λ̂

In this paper we present an algorithm for pricing barrier options in one-dimensional Markov models. The approach rests on the construction of an approximating continuous-time Markov chain that closely follows the dynamics of the given Markov model. We illustrate the method by implementing it for a range of models, including a local Lévy process and a local volatility jump-diffusion. We also pro...