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Learning in evolutionary environments
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A solution to the crucial problem of population degeneration in high-dimensional evolutionary optimization
Three popular evolutionary optimization algorithms are tested on high-dimensional benchmark functions. An important phenomenon responsible for many failures - population degeneration - is discovered. That is, through evolution, the population of searching particles degenerates into a subspace of the search space, and the global optimum is exclusive from the subspace. Subsequently, the search will tend to be confined to this subspace and eventually miss the global optimum. Principal components analysis (PCA) is introduced to discover population degeneration and to remedy its adverse effects. The experiment results reveal that an algorithm's efficacy and efficiency are closely related to the population degeneration phenomenon. Guidelines for improving evolutionary algorithms for high-dimensional global optimization are addressed. An application to highly nonlinear hydrological models demonstrates the efficacy of improved evolutionary algorithms in solving complex practical problems. © 2011 IEEE
Learning in a Landscape: Simulation-building as Reflexive Intervention
This article makes a dual contribution to scholarship in science and
technology studies (STS) on simulation-building. It both documents a specific
simulation-building project, and demonstrates a concrete contribution to
interdisciplinary work of STS insights. The article analyses the struggles that
arise in the course of determining what counts as theory, as model and even as
a simulation. Such debates are especially decisive when working across
disciplinary boundaries, and their resolution is an important part of the work
involved in building simulations. In particular, we show how ontological
arguments about the value of simulations tend to determine the direction of
simulation-building. This dynamic makes it difficult to maintain an interest in
the heterogeneity of simulations and a view of simulations as unfolding
scientific objects. As an outcome of our analysis of the process and
reflections about interdisciplinary work around simulations, we propose a
chart, as a tool to facilitate discussions about simulations. This chart can be
a means to create common ground among actors in a simulation-building project,
and a support for discussions that address other features of simulations
besides their ontological status. Rather than foregrounding the chart's
classificatory potential, we stress its (past and potential) role in discussing
and reflecting on simulation-building as interdisciplinary endeavor. This chart
is a concrete instance of the kinds of contributions that STS can make to
better, more reflexive practice of simulation-building.Comment: 37 page
Learning in Evolutionary Environments
The purpose of this work is to present a sort of short selective guide to an enormous and diverse literature on learning processes in economics. We argue that learning is an ubiquitous characteristic of most economic and social systems but it acquires even greater importance in explicitly evolutionary environments where: a) heterogeneous agents systematically display various forms of "bounded rationality"; b) there is a persistent appearance of novelties, both as exogenous shocks and as the result of technological, behavioural and organisational innovations by the agents themselves; c) markets (and other interaction arrangements) perform as selection mechanisms; d) aggregate regularities are primarily emergent properties stemming from out-of-equilibrium interactions. We present, by means of examples, the most important classes of learning models, trying to show their links and differences, and setting them against a sort of ideal framework of "what one would like to understand about learning...". We put a signifiphasis on learning models in their bare-bone formal structure, but we also refer to the (generally richer) non-formal theorising about the same objects. This allows us to provide an easier mapping of a wide and largely unexplored research agenda.Learning, Evolutionary Environments, Economic Theory, Rationality
Differential evolution based bi-level programming algorithm for computing normalized nash equilibrium
The Generalised Nash Equilibrium Problem (GNEP) is a Nash game with the distinct feature that the feasible strategy set of a player depends on the strategies chosen by all her opponents in the game. This characteristic distinguishes the GNEP from a conventional Nash Game. These shared constraints on each playerâs decision space, being dependent on decisions of others in the game, increases its computational difficulty. A special solution of the GNEP is the Nash Normalized Equilibrium which can be obtained by transforming the GNEP into a bi-level program with an optimal value of zero in the upper level. In this paper, we propose a Differential Evolution based Bi-Level Programming algorithm embodying Stochastic Ranking to handle constraints (DEBLP-SR) to solve the resulting bi-level programming formulation. Numerical examples of GNEPs drawn from the literature are used to illustrate the performance of the proposed algorithm
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