191,137 research outputs found
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Letter From Murray C. McClellan to Emmett L. Bennett Jr., November 8, 1981
No full textBennett describes a set of negatives of the Pylos tablets and speculates if he might be able to photograph the Pylos site.Classic
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Letter from Sterling Dow to Emmett Bennett Jr., undated
No full textDow thanks Bennett for additions to a paper on Pylos tablets and congratulates him on his Fulbright scholarship. (c. 1965)Classic
Benefits of a population: five mechanisms that advantage population-based algorithms
No full textThis paper identifies five distinct mechanisms by which a population-based algorithm might have an advantage over a solo-search algorithm in classical optimization. These mechanisms are illustrated through a number of toy problems. Simulations are presented comparing different search algorithms on these problems. The plausibility of these mechanisms occurring in classical optimization problems is discussed. The first mechanism we consider relies on putting together building blocks from different solutions. This is extended to include problems containing critical variables. The second mechanism is the result of focusing of the search caused by crossover. Also discussed in this context is strong focusing produced by averaging many solutions. The next mechanism to be examined is the ability of a population to act as a low-pass filter of the landscape, ignoring local distractions. The fourth mechanism is a population's ability to search different parts of the fitness landscape, thus hedging against bad luck in the initial position or the decisions it makes. The final mechanism is the opportunity of learning useful parameter values to balance exploration against exploitation
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Letter from Michael Ventris to Emmett L. Bennett, Jr., December 8, 1955
No full textVentris sends a brief example of his current work varityping the Knossos tablets to Bennett to
show him the format. He has chosen to reduce the /B, /C, /Vs when possible, and the keep
them only in cases where readings are unclear or where he and Bennett disagree on the
reading.Classic
Genetic Drift in Genetic Algorithm Selection Schemes
No full textA method for calculating genetic drift in terms of changing population fitness variance is presented. The method allows for an easy comparison of different selection schemes and exact analytical results are derived for traditional generational selection, steady-state selection with varying generation gap, a simple model of Eshelman's CHC algorithm, and evolution strategies. The effects of changing genetic drift on the convergence of a GA are demonstrated empirically
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Letter From Elizabeth Jane Wayland Barber to Emmett L. Bennett Jr., July 16, 1961
No full textWayland thanks Bennett for seeing to her travel to the Racine conference and for his providing Brice's book on Linear A inscriptions. She relates difficulty of using computers c. 1960 at Cal-Tech to print indexes.Classic
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Letter from Emmett Bennett Jr. to William C. Brice, June 22, 1983
No full textBennett offers to Brice a critique of Daniel Was's Minoan Taxation article.Classic
Owen Bennett
No full text"Cpl. Owen Bennett NX161267 1st. Aust. B.I.P.O.D. March 1941 - October 1943 Adelaide River A'Sect. Katherine C'Sect. Originally [7MD Petrol Convoy]".Corporal Owen Bennett. NX161267 1st Australian Bulk Issue Petrol and Oil Depot, March 1941 - October 1943. Adelaide River A' Section, Katherine C' Section Originally [7th Military District Petrol Convoy]
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Letter from Emmett Bennett Jr. to William C. Brice, June 24, 1983
No full textBennett writes at length criticisms on Daniel Was's Linear A research, particularly doubting his interpretation of medimnos and metretees and several readings of Linear A tablets.Classic
Modelling the Dynamics of a Steady State Genetic Algorithm
No full textA comparison is made between the dynamics of steady state and generational genetic algorithms using the statistical mechanics approach developed by Prugel-Bennett, Shapiro and Rattray. It is shown that the loss of variance of the population under steady state selection - genetic drift - occurs at twice the rate of generational selection. By considering a simple ones counting problem with selection and mutation, it is shown that, with weak selection, the steady state genetic algorithm can reproduce the dynamics of the generational genetic algorithm at half the computational cost in terms of function evaluations
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