Component-based thinking in designing interactive multiobjective evolutionary methods
Lárraga, G., & Miettinen, K. (2023). Component-based thinking in designing interactive multiobjective evolutionary methods. In GECCO '23 Companion : Proceedings of the Companion Conference on Genetic and Evolutionary Computation (pp. 1693-1702). ACM. https://doi.org/10.1145/3583133.3596307
© 2023 Copyright held by the owner/author(s).
Multiobjective optimization problems have multiple conflicting objective functions to be optimized simultaneously. They have many Pareto optimal solutions representing different trade-offs, and a decision-maker needs to find the most preferred one. Although most multiobjective evolutionary algorithms approximate the Pareto optimal set, their variants incorporate preference information to focus on a subset of solutions that interest the decision-maker. Interactive methods allow decision-makers to provide preference information iteratively during the solution process, enabling them to learn about available solutions and their preferences' feasibility. Nevertheless, most interactive evolutionary methods do not sufficiently support the decision-maker in finding the most preferred solution and may be cognitively too demanding. We propose a framework for designing and implementing interactive evolutionary methods. It contains algorithmic components based on similarities in the structure of existing preference-based evolutionary algorithms and decision-makers' needs during interaction. The components can be combined in different ways to create new interactive methods or to instantiate the existing ones. We show an example of the implementation of the proposed framework composed of three elements: a graphical user interface, a database, and a set of algorithmic components. The resulting software can be utilized to develop new methods and increase their usability in real-world applications. ...
Parent publication ISBN979-8-4007-0120-7
ConferenceGenetic and Evolutionary Computation Conference
Is part of publicationGECCO '23 Companion : Proceedings of the Companion Conference on Genetic and Evolutionary Computation
Publication in research information system
MetadataShow full item record
Related funder(s)Academy of Finland
Funding program(s)Academy Project, AoF
Additional information about fundingThis research was supported by the Academy of Finland (grant number 322221). The research is related to the thematic research area DEMO (Decision Analytics utilizing Causal Models and Multiobjective Optimization, jyu.fi/demo) of the University of Jyväskylä
Showing items with similar title or keywords.
Potential of interactive multiobjective optimization in supporting the design of a groundwater biodenitrification process Saccani, Giulia; Hakanen, Jussi; Sindhya, Karthi; Ojalehto, Vesa; Hartikainen, Markus; Antonelli, Manuela; Miettinen, Kaisa (Elsevier, 2020)The design of water treatment plants requires simultaneous analysis of technical, economic and environmental aspects, identified by multiple conflicting objectives. We demonstrated the advantages of an interactive ...
A Visualization Technique for Accessing Solution Pool in Interactive Methods of Multiobjective Optimization Filatovas, Ernestas; Podkopaev, Dmitry; Kurasova, Olga (Universitatea Agora, 2015)Interactive methods of multiobjective optimization repetitively derive Pareto optimal solutions based on decision maker's preference information and present the obtained solutions for his/her consideration. Some interactive ...
Comparing interactive evolutionary multiobjective optimization methods with an artificial decision maker Afsar, Bekir; Ruiz, Ana B.; Miettinen, Kaisa (Springer Science+Business Media, 2023)Solving multiobjective optimization problems with interactive methods enables a decision maker with domain expertise to direct the search for the most preferred trade-offs with preference information and learn about the ...
Probabilistic Selection Approaches in Decomposition-based Evolutionary Algorithms for Offline Data-Driven Multiobjective Optimization Mazumdar, Atanu; Chugh, Tinkle; Hakanen, Jussi; Miettinen, Kaisa (IEEE, 2022)In offline data-driven multiobjective optimization, no new data is available during the optimization process. Approximation models, also known as surrogates, are built using the provided offline data. A multiobjective ...
Misitano, Giovanni; Saini, Bhupinder Singh; Afsar, Bekir; Shavazipour, Babooshka; Miettinen Kaisa (Institute of Electrical and Electronics Engineers (IEEE), 2021)Interactive multiobjective optimization methods incorporate preferences from a human decision maker in the optimization process iteratively. This allows the decision maker to focus on a subset of solutions, learn about the ...