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Collective preferences in evolutionary multi-objective optimization: techniques and potential contributions of collective intelligence

Published: 13 April 2015 Publication History

Abstract

This paper reviews suitable techniques of interactive and preference-based evolutionary multi-objective algorithms to achieve feasible solutions in Pareto-optimal front. We discuss about possible advantages of collective environments to aggregate consistent preferences in the optimization process. Decision maker can highlight the regions of Pareto frontier that are more relevant to him and focus the search only on those areas previously selected. In addition, interactive and cooperative genetic algorithms work on refining users' preferences throughout the optimization process to improve the reference point or fitness function. Nevertheless, expressing preferences from a unique or small group of decision makers may raise unilateral choices issues and pour hints in terms of search parameter. Supported by a large group of human interaction, collective intelligence is suggested to enhance multi-objective results and explore a wider variety of answers.

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cover image ACM Conferences
SAC '15: Proceedings of the 30th Annual ACM Symposium on Applied Computing
April 2015
2418 pages
ISBN:9781450331968
DOI:10.1145/2695664
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 13 April 2015

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Author Tags

  1. collective intelligence
  2. evolutionary multi-objective optimization algorithms
  3. preferences
  4. reference points

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SAC 2015
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SAC 2015: Symposium on Applied Computing
April 13 - 17, 2015
Salamanca, Spain

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SAC '15 Paper Acceptance Rate 291 of 1,211 submissions, 24%;
Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

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  • (2022)Group Multi-Objective Optimization Under Imprecision and Uncertainty Using a Novel Interval Outranking ApproachGroup Decision and Negotiation10.1007/s10726-022-09789-831:5(945-994)Online publication date: 24-Jun-2022
  • (2021)A New Approach to Group Multi-Objective Optimization under Imperfect Information and Its Application to Project Portfolio OptimizationApplied Sciences10.3390/app1110457511:10(4575)Online publication date: 17-May-2021
  • (2020)Automated Discovery of Relationships, Models, and Principles in EcologyFrontiers in Ecology and Evolution10.3389/fevo.2020.5301358Online publication date: 11-Dec-2020
  • (2020)Extending Collective Intelligence Evolutionary Algorithms: A Facility Location Problem Application2020 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC48606.2020.9185523(1-8)Online publication date: Jul-2020
  • (2019)Improving Algorithm Response to Preference Changes in Multiobjective Optimisation Using Archives2019 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2019.8789949(2442-2449)Online publication date: Jun-2019
  • (2018)Interactive multiobjective optimisationProceedings of the Genetic and Evolutionary Computation Conference10.1145/3205455.3205624(761-768)Online publication date: 2-Jul-2018

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