this paper "ergodic" is used in a very weak sense, as it is not proposed, theoretically or practically, that all states of the system are actually to be visited

s: Jan. 1992 -- Dec. 1994 ffl CTI: Current Technology Index Jan./Feb. 1993 -- Jan./Feb. 1994 ffl DAI: Dissertation Abstracts International: Vol. 53 No. 1 -- Vol. 55 No. 4 (1994) ffl EEA: Electrical & Electronics Abstracts: Jan. 1991 -- Dec. 1994 ffl P: Index to Scientific & Technical Proceedings: Jan. 1986 -- Feb. 1995 (except Nov. 1994) ffl EI A: The Engineering Index Annual: 1987 -- 1992 ffl EI M: The Engineering Index Monthly: Jan. 1993 -- Dec. 1994 The following GA researchers have already kindly supplied their complete autobibliographies and/or proofread references to their papers: Dan Adler, Patrick Argos, Jarmo T. Alander, James E. Baker, Wolfgang Banzhaf, Ralf Bruns, I. L. Bukatova, Thomas Back, Yuval Davidor, Dipankar Dasgupta, Marco Dorigo, Bogdan Filipic, Terence C. Fogarty, David B. Fogel, Toshio Fukuda, Hugo de Garis, Robert C. Glen, David E. Goldberg, Martina Gorges-Schleuter, Jeffrey Horn, Aristides T. Hatjimihail, Mark J. Jakiela, Richard S. Judson, Akihiko Konaga...

A model of population rule learning is formulated and estimated using experimental data. When predicting the population distribution of choices and accounting for the number of parameters, the population rule learning model is much better than aggregation of individually estimated rule learning models. Further, rule learning is a statistically significant and important phenomena even when focusing on population statistics, and is much better than one-rule learning dynamics. 2001 Elsevier Science B.V. All rights reserved. JEL classification: C15; C52; C72 Keywords: Rules; Learning; Games; Experimental; Testing 1. Introduction Recent learning research in one-shot games can be divided into two domains: (i) population learning or evolutionary dynamics as typified by replicator dynamics, 1 and (ii) individual learning. 23 The first domain focuses on how the population distribution of play changes over time, while the second domain focuses on how an individual's behavior changes over...

INTRODUCTION In this paper we consider Simulated Annealing algorithms (SA in what follows) applied to continuous global optimization problems, i.e. problems with the following form f = min x2X f(x); (1.1) where X ` ! n is a continuous domain, often assumed to be compact, which, combined with the continuity or lower semicontinuity of f , guarantees the existence of the minimum value f . SA algorithms are based on an analogy with a physical phenomenon: while at high temperatures the molecules in a liquid move freely, if the temperature is slowly decreased the thermal mobility of the molecules is lost and they form a pure crystal which also corresponds to a state of minimum energy. If the temperature is decreased too quickly (the so called quenching) a liquid metal rather ends up in a polycrystalline or amorphous state with

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... these new methods, we develop a prototype, called Novel (Nonlinear Optimization Via External Lead), that solves nonlinear constrained and unconstrained problems in a unified framework. We show experimental results in applying Novel to solve nonlinear optimization problems, including (a) the learning of feedforward neural networks, (b) the design of quadrature-mirror-filter digital filter banks, (c) the satisfiability problem, (d) the maximum satisfiability problem, and (e) the design of multiplierless quadrature-mirror-filter digital filter banks. Our method achieves better solutions than existing methods, or achieves solutions of the same quality but at a lower cost.

The authors thank three anonymous referees and the Editor Badi Baltagi for several useful comments. This work was supported by the Bank of Canada and by grants from the Canadian Network of Centres of Excellence [program on Mathematics

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This paper proposes a method for comparing and combining conditional quantile forecasts in an out-of-sample framework. We construct a Conditional Quantile Forecast Encompassing (CQFE) test as a Wald-type test of superior predictive ability. Rejection of CQFE provides a basis for combination of conditional quantile forecasts. Central features of our encompassing test are: (1) the use of the ‘tick ’ loss function; (2) a conditional, ratherthanunconditional approach to out-of-sample evaluation, and, (3) the derivation of our test in an environment with non-vanishing estimation uncertainty. Some of the advantages of our approach are that it allows the forecasts to be generated by using general estimation procedures and that it is applicable when the forecasts are based on both nested and non-nested models. The test is also relatively easy to implement using standard GMM techniques. An empirical application to Value-at-Risk evaluation illustrates the usefulness of our method.

recherche sur la société et la culture (Québec), and the Fonds de recherche sur la nature et les technologies (Québec). This paper was also partly written at the Centre de recherche en Économie et Statistique (INSEE, Paris), the Technische Universität Dresden (Fakultät Wirtschaftswissenschaften) and the Finance Division at the University of British Columbia. Centre interuniversitaire sur le risque, les politiques économiques et l’emploi (CIRPÉE), CIRANO, and Département de finance et assurance, Université Laval. Mailing address: Département de finance et assurance, Pavillon