Machine decision makers as a laboratory for interactive EMO

Manuel López-Ibáñez*, Joshua Knowles

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)
140 Downloads (Pure)

Abstract

A key challenge, perhaps the central challenge, of multiobjective optimization is how to deal with candidate solutions that are ultimately evaluated by the hidden or unknown preferences of a human decision maker (DM) who understands and cares about the optimization problem. Alternative ways of addressing this challenge exist but perhaps the favoured one currently is the interactive approach (proposed in various forms). Here, an evolutionary multi-objective optimization algorithm (EMOA) is controlled by a series of interactions with the DMso that preferences can be elicited and the direction of search controlled. MCDM has a key role to play in designing and evaluating these approaches, particularly in testing them with real DMs, but so far quantitative assessment of interactive EMOAs has been limited. In this paper, we propose a conceptual framework for this problem of quantitative assessment, based on the definition of machine decision makers (machine DMs), made somewhat realistic by the incorporation of various non-idealities. The machine DM proposed here draws from earlier models of DM biases and inconsistencies in the MCDM literature. As a practical illustration of our approach, we use the proposed machine DM to study the performance of an interactive EMOA, and discuss how this framework could help in the evaluation and development of better interactive EMOAs.

Original languageEnglish
Title of host publicationEvolutionary multi-criterion optimization
Subtitle of host publication8th international conference, EMO 2015 Guimarães, Portugal, March 29 - April 1, 2015 proceedings, Part II
PublisherSpringer
Pages295-309
Number of pages15
Volume9019
ISBN (Print)9783319158914
DOIs
Publication statusPublished - 18 Mar 2015
Event8th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2015 - Guimarães, Portugal
Duration: 29 Mar 20151 Apr 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9019
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference8th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2015
Country/TerritoryPortugal
CityGuimarães
Period29/03/151/04/15

Keywords

  • Artificial decision makers
  • Interactive EMO
  • Machine decision makers
  • MCDM
  • Performance assessment

ASJC Scopus subject areas

  • General Computer Science
  • Theoretical Computer Science

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