Alexander Kosorukoff

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Human-based genetic algorithms (HBGA) use both human evaluation and innovation to optimize a population of solutions (Kosorukoff, 2001). The novel contribution of HBGAs is an introduction of human-based innovation operators. However, there was no attempt to measure the effect of human-based innovation operators on the overall performance of GAs(More)
In contrast to previous steady-state analyses of the O(2)-responsive transcriptome, here we examined the dynamics of the response to short-term anaerobiosis (2 generations) in both catabolite-repressed (glucose) and derepressed (galactose) cells, assessed the specific role that Msn2 and Msn4 play in mediating the response, and identified gene networks using(More)
Traditional areas of application of genetic algorithms (GA) are engineering and technology. Success of genetic algorithms there is well known. This paper explores the use of genetic algorithms as models to in-uence the design of organization. In particular , we outline the concept of evolutionary organization process based on two recent cases: the Teamwork(More)
We conducted a comprehensive genomic analysis of the temporal response of yeast to anaerobiosis (six generations) and subsequent aerobic recovery ( approximately 2 generations) to reveal metabolic-state (galactose versus glucose)-dependent differences in gene network activity and function. Analysis of variance showed that far fewer genes responded (raw P(More)
The number of tness evaluations determines the eeciency of a GA. This research suggests incremental evaluation which, if applicable, substantially reduces the complexity o f e v al-uation of some individuals. Incremental evaluation and rational choice of operator based on utility maximization gives a twofold reduction in computational cost needed to(More)
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