A steady-state genetic algorithm with resampling for noisy inventory control

Noisy fitness functions occur in many practical applications of evolutionary computation. A standard technique for solving these problems is fitness resampling but this may be inefficient or need a large population, and combined with elitism it may overvalue chromosomes or reduce genetic diversity. We describe a simple new resampling technique called Greedy Average Sampling for steady-state genetic algorithms such as GENITOR. It requires an extra runtime parameter to be tuned, but does not need a large population or assumptions on noise distributions. In experiments on a well-known Inventory Control problem it performed a large number of samples on the best chromosomes yet only a small number on average, and was more effective than four other tested technique

An experimental investigation of discordant isotopic ages in zircons

info:eu-repo/semantics/publishe

Precambrian age determination in the Western San Gabriel Mountains, California

http://www.jstor.org/stable/30066152info:eu-repo/semantics/publishe

Empty sella developing during thyroxine therapy in a patient with primary hypothyroidism and hyperprolactinaemia.

A 35 year old woman presented with severe primary hypothyroidism and galactorrhea. A very high prolactin level was also detected and computerized tomography scan of the sellar region demonstrated an enlarged pituitary gland associated with contrast enhancement. Replacement therapy with thyroxine corrected both biochemical and clinical abnormalities but empty sella developed during this therapy. It is concluded that empty sella may be related to thyroxine-induced shrinkage of lactotroph and/or thyrotroph cell hyperplasia