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I-EMO: An Interactive Evolutionary Multi-objective Optimization Tool

  • Kalyanmoy Deb
  • Shamik Chaudhuri
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3776)

Abstract

With the advent of efficient techniques for multi-objective evolutionary optimization (EMO), real-world search and optimization problems are being increasingly solved for multiple conflicting objectives. During the past decade, most emphasis has been spent on finding the complete Pareto-optimal set, although EMO researchers were always aware of the importance of procedures which would help choose one particular solution from the Pareto-optimal set for implementation. This is also one of the main issues on which the classical and EMO philosophies are divided on. In this paper, we address this long-standing issue and suggest an interactive EMO procedure which, for the first time, will involve a decision-maker in the evolutionary optimization process and help choose a single solution at the end. This study is the culmination of many year’s of research on EMO and would hopefully encourage both practitioners and researchers to pay more attention in viewing the multi-objective optimization as an aggregate task of optimization and decision-making.

Keywords

Decision Maker Multiobjective Optimization Edition Edition Reference Point Approach Evolutionary Optimization Process 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Kalyanmoy Deb
    • 1
  • Shamik Chaudhuri
    • 1
  1. 1.Kanpur Genetic Algorithms Laboratory (KanGAL)Indian Institute of Technology, KanpurKanpurIndia

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