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How multi-objective genetic algorithms handle lack of data, sparse data and excess data: evaluation of some recent case studies in the materials domain

Authors

  • N. Chakraborti

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    1. Department of Metallurgical & Materials Engineering, Indian Institute of Technology, Kharagpur 721 302, India
    • Department of Metallurgical & Materials Engineering, Indian Institute of Technology, Kharagpur 721 302, India
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Abstract

An overview of multi-objective optimization and the associated concept of Pareto-optimality are elucidated in detail, keeping the biologically inspired genetic algorithms in perspective. The effective role of the genetic algorithms in handling three different kinds of data driven models where the decision has to be made from (i) no data (ii) excess data or (iii) sparse data are elaborated through three materials engineering applications, where other strategies like inverse modeling, neural network and data mining have worked in tandem with the multi-objective genetic algorithms. Copyright © 2009 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 1: 000-000, 2009

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