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Heterogeneous Learning Using Genetic Algorithms

Heterogeneous Learning Using Genetic Algorithms

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Author(s): T. Vallee (LEN, Nantes University, France)
Copyright: 2007
Pages: 17
Source title: Handbook of Research on Nature-Inspired Computing for Economics and Management
Source Author(s)/Editor(s): Jean-Philippe Rennard (Grenoble Graduate School of Business, France)
DOI: 10.4018/978-1-59140-984-7.ch018

Keywords: Biologically-Inspired Computing / Business Science Reference / Industrial Informatics / Medicine & Healthcare

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Abstract

The goal of this chapter is twofold. First, assuming that all agents belong to a genetic population, the evolution of inflation learning will be studied using a heterogeneous genetic learning process. Second, by using real-floating-point coding and different genetic operators, the quality of the learning tools and their possible impact on the learning process will be examined.

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