The Experts below are selected from a list of 5676 Experts worldwide ranked by ideXlab platform

Kiyoshi Tanaka - One of the best experts on this subject based on the ideXlab platform.

  • approximating Pareto Set topology by cubic interpolation on bi objective problems
    International Conference on Evolutionary Multi-criterion Optimization, 2019
    Co-Authors: Yuri Marca, Hernan Aguirre, Saul Zapotecas Martinez, Arnaud Liefooghe, Bilel Derbel, Sebastien Verel, Kiyoshi Tanaka
    Abstract:

    Difficult Pareto Set topology refers to multi-objective problems with geometries of the Pareto Set such that neighboring optimal solutions in objective space differ in several or all variables in decision space. These problems can present a tough challenge for evolutionary multi-objective algorithms to find a good approximation of the optimal Pareto Set well-distributed in decision and objective space. One important challenge optimizing these problems is to keep or restore diversity in decision space. In this work, we propose a method that learns a model of the topology of the solutions in the population by performing parametric spline interpolations for all variables in decision space. We use Catmull-Rom parametric curves as they allow us to deal with any dimension in decision space. The proposed method is appropriated for bi-objective problems since their optimal Set is a one-dimensional curve according to the Karush-Kuhn-Tucker condition. Here, the proposed method is used to promote restarts from solutions generated by the model. We study the effectiveness of the proposed method coupled to NSGA-II and two variations of MOEA/D on problems with difficult Pareto Set topology. These algorithms approach very differently the Pareto Set. We argue and discuss their behavior and its implications for model building.

  • EMO - Approximating Pareto Set Topology by Cubic Interpolation on Bi-objective Problems.
    Lecture Notes in Computer Science, 2019
    Co-Authors: Yuri Marca, Hernan Aguirre, Saul Zapotecas Martinez, Arnaud Liefooghe, Bilel Derbel, Sebastien Verel, Kiyoshi Tanaka
    Abstract:

    Difficult Pareto Set topology refers to multi-objective problems with geometries of the Pareto Set such that neighboring optimal solutions in objective space differ in several or all variables in decision space. These problems can present a tough challenge for evolutionary multi-objective algorithms to find a good approximation of the optimal Pareto Set well-distributed in decision and objective space. One important challenge optimizing these problems is to keep or restore diversity in decision space. In this work, we propose a method that learns a model of the topology of the solutions in the population by performing parametric spline interpolations for all variables in decision space. We use Catmull-Rom parametric curves as they allow us to deal with any dimension in decision space. The proposed method is appropriated for bi-objective problems since their optimal Set is a one-dimensional curve according to the Karush-Kuhn-Tucker condition. Here, the proposed method is used to promote restarts from solutions generated by the model. We study the effectiveness of the proposed method coupled to NSGA-II and two variations of MOEA/D on problems with difficult Pareto Set topology. These algorithms approach very differently the Pareto Set. We argue and discuss their behavior and its implications for model building.

  • Pareto dominance based moeas on problems with difficult Pareto Set topologies
    Genetic and Evolutionary Computation Conference, 2018
    Co-Authors: Yuri Marca, Hernan Aguirre, Arnaud Liefooghe, Bilel Derbel, Sebastien Verel, Saul Zapotecas, Kiyoshi Tanaka
    Abstract:

    Despite the extensive application of multi-objective evolutionary algorithms (MOEAs) to solve multi-objective optimization problems (MOPs), understanding their working principles is still open to research. One of the most popular and successful MOEA approaches is based on Pareto dominance and its relaxed version, Pareto ϵ-dominance. However, such approaches have not been sufficiently studied in problems of increased complexity. In this work, we study the effects of the working mechanisms of the various components of these algorithms on test problems with difficult Pareto Set topologies. We focus on separable unimodal and multimodal functions with 2, 3, and 4 objectives, all having difficult Pareto Set topologies. Our experimental study provides some interesting and useful insights to understand better Pareto dominance-based MOEAs.

  • GECCO (Companion) - Pareto dominance-based MOEAs on problems with difficult Pareto Set topologies
    Proceedings of the Genetic and Evolutionary Computation Conference Companion on - GECCO '18, 2018
    Co-Authors: Yuri Marca, Hernan Aguirre, Arnaud Liefooghe, Bilel Derbel, Sebastien Verel, Saul Zapotecas, Kiyoshi Tanaka
    Abstract:

    Despite the extensive application of multi-objective evolutionary algorithms (MOEAs) to solve multi-objective optimization problems (MOPs), understanding their working principles is still open to research. One of the most popular and successful MOEA approaches is based on Pareto dominance and its relaxed version, Pareto ϵ-dominance. However, such approaches have not been sufficiently studied in problems of increased complexity. In this work, we study the effects of the working mechanisms of the various components of these algorithms on test problems with difficult Pareto Set topologies. We focus on separable unimodal and multimodal functions with 2, 3, and 4 objectives, all having difficult Pareto Set topologies. Our experimental study provides some interesting and useful insights to understand better Pareto dominance-based MOEAs.

Yuri Marca - One of the best experts on this subject based on the ideXlab platform.

  • approximating Pareto Set topology by cubic interpolation on bi objective problems
    International Conference on Evolutionary Multi-criterion Optimization, 2019
    Co-Authors: Yuri Marca, Hernan Aguirre, Saul Zapotecas Martinez, Arnaud Liefooghe, Bilel Derbel, Sebastien Verel, Kiyoshi Tanaka
    Abstract:

    Difficult Pareto Set topology refers to multi-objective problems with geometries of the Pareto Set such that neighboring optimal solutions in objective space differ in several or all variables in decision space. These problems can present a tough challenge for evolutionary multi-objective algorithms to find a good approximation of the optimal Pareto Set well-distributed in decision and objective space. One important challenge optimizing these problems is to keep or restore diversity in decision space. In this work, we propose a method that learns a model of the topology of the solutions in the population by performing parametric spline interpolations for all variables in decision space. We use Catmull-Rom parametric curves as they allow us to deal with any dimension in decision space. The proposed method is appropriated for bi-objective problems since their optimal Set is a one-dimensional curve according to the Karush-Kuhn-Tucker condition. Here, the proposed method is used to promote restarts from solutions generated by the model. We study the effectiveness of the proposed method coupled to NSGA-II and two variations of MOEA/D on problems with difficult Pareto Set topology. These algorithms approach very differently the Pareto Set. We argue and discuss their behavior and its implications for model building.

  • EMO - Approximating Pareto Set Topology by Cubic Interpolation on Bi-objective Problems.
    Lecture Notes in Computer Science, 2019
    Co-Authors: Yuri Marca, Hernan Aguirre, Saul Zapotecas Martinez, Arnaud Liefooghe, Bilel Derbel, Sebastien Verel, Kiyoshi Tanaka
    Abstract:

    Difficult Pareto Set topology refers to multi-objective problems with geometries of the Pareto Set such that neighboring optimal solutions in objective space differ in several or all variables in decision space. These problems can present a tough challenge for evolutionary multi-objective algorithms to find a good approximation of the optimal Pareto Set well-distributed in decision and objective space. One important challenge optimizing these problems is to keep or restore diversity in decision space. In this work, we propose a method that learns a model of the topology of the solutions in the population by performing parametric spline interpolations for all variables in decision space. We use Catmull-Rom parametric curves as they allow us to deal with any dimension in decision space. The proposed method is appropriated for bi-objective problems since their optimal Set is a one-dimensional curve according to the Karush-Kuhn-Tucker condition. Here, the proposed method is used to promote restarts from solutions generated by the model. We study the effectiveness of the proposed method coupled to NSGA-II and two variations of MOEA/D on problems with difficult Pareto Set topology. These algorithms approach very differently the Pareto Set. We argue and discuss their behavior and its implications for model building.

  • Pareto dominance based moeas on problems with difficult Pareto Set topologies
    Genetic and Evolutionary Computation Conference, 2018
    Co-Authors: Yuri Marca, Hernan Aguirre, Arnaud Liefooghe, Bilel Derbel, Sebastien Verel, Saul Zapotecas, Kiyoshi Tanaka
    Abstract:

    Despite the extensive application of multi-objective evolutionary algorithms (MOEAs) to solve multi-objective optimization problems (MOPs), understanding their working principles is still open to research. One of the most popular and successful MOEA approaches is based on Pareto dominance and its relaxed version, Pareto ϵ-dominance. However, such approaches have not been sufficiently studied in problems of increased complexity. In this work, we study the effects of the working mechanisms of the various components of these algorithms on test problems with difficult Pareto Set topologies. We focus on separable unimodal and multimodal functions with 2, 3, and 4 objectives, all having difficult Pareto Set topologies. Our experimental study provides some interesting and useful insights to understand better Pareto dominance-based MOEAs.

  • GECCO (Companion) - Pareto dominance-based MOEAs on problems with difficult Pareto Set topologies
    Proceedings of the Genetic and Evolutionary Computation Conference Companion on - GECCO '18, 2018
    Co-Authors: Yuri Marca, Hernan Aguirre, Arnaud Liefooghe, Bilel Derbel, Sebastien Verel, Saul Zapotecas, Kiyoshi Tanaka
    Abstract:

    Despite the extensive application of multi-objective evolutionary algorithms (MOEAs) to solve multi-objective optimization problems (MOPs), understanding their working principles is still open to research. One of the most popular and successful MOEA approaches is based on Pareto dominance and its relaxed version, Pareto ϵ-dominance. However, such approaches have not been sufficiently studied in problems of increased complexity. In this work, we study the effects of the working mechanisms of the various components of these algorithms on test problems with difficult Pareto Set topologies. We focus on separable unimodal and multimodal functions with 2, 3, and 4 objectives, all having difficult Pareto Set topologies. Our experimental study provides some interesting and useful insights to understand better Pareto dominance-based MOEAs.

Sebastien Verel - One of the best experts on this subject based on the ideXlab platform.

  • approximating Pareto Set topology by cubic interpolation on bi objective problems
    International Conference on Evolutionary Multi-criterion Optimization, 2019
    Co-Authors: Yuri Marca, Hernan Aguirre, Saul Zapotecas Martinez, Arnaud Liefooghe, Bilel Derbel, Sebastien Verel, Kiyoshi Tanaka
    Abstract:

    Difficult Pareto Set topology refers to multi-objective problems with geometries of the Pareto Set such that neighboring optimal solutions in objective space differ in several or all variables in decision space. These problems can present a tough challenge for evolutionary multi-objective algorithms to find a good approximation of the optimal Pareto Set well-distributed in decision and objective space. One important challenge optimizing these problems is to keep or restore diversity in decision space. In this work, we propose a method that learns a model of the topology of the solutions in the population by performing parametric spline interpolations for all variables in decision space. We use Catmull-Rom parametric curves as they allow us to deal with any dimension in decision space. The proposed method is appropriated for bi-objective problems since their optimal Set is a one-dimensional curve according to the Karush-Kuhn-Tucker condition. Here, the proposed method is used to promote restarts from solutions generated by the model. We study the effectiveness of the proposed method coupled to NSGA-II and two variations of MOEA/D on problems with difficult Pareto Set topology. These algorithms approach very differently the Pareto Set. We argue and discuss their behavior and its implications for model building.

  • EMO - Approximating Pareto Set Topology by Cubic Interpolation on Bi-objective Problems.
    Lecture Notes in Computer Science, 2019
    Co-Authors: Yuri Marca, Hernan Aguirre, Saul Zapotecas Martinez, Arnaud Liefooghe, Bilel Derbel, Sebastien Verel, Kiyoshi Tanaka
    Abstract:

    Difficult Pareto Set topology refers to multi-objective problems with geometries of the Pareto Set such that neighboring optimal solutions in objective space differ in several or all variables in decision space. These problems can present a tough challenge for evolutionary multi-objective algorithms to find a good approximation of the optimal Pareto Set well-distributed in decision and objective space. One important challenge optimizing these problems is to keep or restore diversity in decision space. In this work, we propose a method that learns a model of the topology of the solutions in the population by performing parametric spline interpolations for all variables in decision space. We use Catmull-Rom parametric curves as they allow us to deal with any dimension in decision space. The proposed method is appropriated for bi-objective problems since their optimal Set is a one-dimensional curve according to the Karush-Kuhn-Tucker condition. Here, the proposed method is used to promote restarts from solutions generated by the model. We study the effectiveness of the proposed method coupled to NSGA-II and two variations of MOEA/D on problems with difficult Pareto Set topology. These algorithms approach very differently the Pareto Set. We argue and discuss their behavior and its implications for model building.

  • Pareto dominance based moeas on problems with difficult Pareto Set topologies
    Genetic and Evolutionary Computation Conference, 2018
    Co-Authors: Yuri Marca, Hernan Aguirre, Arnaud Liefooghe, Bilel Derbel, Sebastien Verel, Saul Zapotecas, Kiyoshi Tanaka
    Abstract:

    Despite the extensive application of multi-objective evolutionary algorithms (MOEAs) to solve multi-objective optimization problems (MOPs), understanding their working principles is still open to research. One of the most popular and successful MOEA approaches is based on Pareto dominance and its relaxed version, Pareto ϵ-dominance. However, such approaches have not been sufficiently studied in problems of increased complexity. In this work, we study the effects of the working mechanisms of the various components of these algorithms on test problems with difficult Pareto Set topologies. We focus on separable unimodal and multimodal functions with 2, 3, and 4 objectives, all having difficult Pareto Set topologies. Our experimental study provides some interesting and useful insights to understand better Pareto dominance-based MOEAs.

  • GECCO (Companion) - Pareto dominance-based MOEAs on problems with difficult Pareto Set topologies
    Proceedings of the Genetic and Evolutionary Computation Conference Companion on - GECCO '18, 2018
    Co-Authors: Yuri Marca, Hernan Aguirre, Arnaud Liefooghe, Bilel Derbel, Sebastien Verel, Saul Zapotecas, Kiyoshi Tanaka
    Abstract:

    Despite the extensive application of multi-objective evolutionary algorithms (MOEAs) to solve multi-objective optimization problems (MOPs), understanding their working principles is still open to research. One of the most popular and successful MOEA approaches is based on Pareto dominance and its relaxed version, Pareto ϵ-dominance. However, such approaches have not been sufficiently studied in problems of increased complexity. In this work, we study the effects of the working mechanisms of the various components of these algorithms on test problems with difficult Pareto Set topologies. We focus on separable unimodal and multimodal functions with 2, 3, and 4 objectives, all having difficult Pareto Set topologies. Our experimental study provides some interesting and useful insights to understand better Pareto dominance-based MOEAs.

Bilel Derbel - One of the best experts on this subject based on the ideXlab platform.

  • approximating Pareto Set topology by cubic interpolation on bi objective problems
    International Conference on Evolutionary Multi-criterion Optimization, 2019
    Co-Authors: Yuri Marca, Hernan Aguirre, Saul Zapotecas Martinez, Arnaud Liefooghe, Bilel Derbel, Sebastien Verel, Kiyoshi Tanaka
    Abstract:

    Difficult Pareto Set topology refers to multi-objective problems with geometries of the Pareto Set such that neighboring optimal solutions in objective space differ in several or all variables in decision space. These problems can present a tough challenge for evolutionary multi-objective algorithms to find a good approximation of the optimal Pareto Set well-distributed in decision and objective space. One important challenge optimizing these problems is to keep or restore diversity in decision space. In this work, we propose a method that learns a model of the topology of the solutions in the population by performing parametric spline interpolations for all variables in decision space. We use Catmull-Rom parametric curves as they allow us to deal with any dimension in decision space. The proposed method is appropriated for bi-objective problems since their optimal Set is a one-dimensional curve according to the Karush-Kuhn-Tucker condition. Here, the proposed method is used to promote restarts from solutions generated by the model. We study the effectiveness of the proposed method coupled to NSGA-II and two variations of MOEA/D on problems with difficult Pareto Set topology. These algorithms approach very differently the Pareto Set. We argue and discuss their behavior and its implications for model building.

  • EMO - Approximating Pareto Set Topology by Cubic Interpolation on Bi-objective Problems.
    Lecture Notes in Computer Science, 2019
    Co-Authors: Yuri Marca, Hernan Aguirre, Saul Zapotecas Martinez, Arnaud Liefooghe, Bilel Derbel, Sebastien Verel, Kiyoshi Tanaka
    Abstract:

    Difficult Pareto Set topology refers to multi-objective problems with geometries of the Pareto Set such that neighboring optimal solutions in objective space differ in several or all variables in decision space. These problems can present a tough challenge for evolutionary multi-objective algorithms to find a good approximation of the optimal Pareto Set well-distributed in decision and objective space. One important challenge optimizing these problems is to keep or restore diversity in decision space. In this work, we propose a method that learns a model of the topology of the solutions in the population by performing parametric spline interpolations for all variables in decision space. We use Catmull-Rom parametric curves as they allow us to deal with any dimension in decision space. The proposed method is appropriated for bi-objective problems since their optimal Set is a one-dimensional curve according to the Karush-Kuhn-Tucker condition. Here, the proposed method is used to promote restarts from solutions generated by the model. We study the effectiveness of the proposed method coupled to NSGA-II and two variations of MOEA/D on problems with difficult Pareto Set topology. These algorithms approach very differently the Pareto Set. We argue and discuss their behavior and its implications for model building.

  • Pareto dominance based moeas on problems with difficult Pareto Set topologies
    Genetic and Evolutionary Computation Conference, 2018
    Co-Authors: Yuri Marca, Hernan Aguirre, Arnaud Liefooghe, Bilel Derbel, Sebastien Verel, Saul Zapotecas, Kiyoshi Tanaka
    Abstract:

    Despite the extensive application of multi-objective evolutionary algorithms (MOEAs) to solve multi-objective optimization problems (MOPs), understanding their working principles is still open to research. One of the most popular and successful MOEA approaches is based on Pareto dominance and its relaxed version, Pareto ϵ-dominance. However, such approaches have not been sufficiently studied in problems of increased complexity. In this work, we study the effects of the working mechanisms of the various components of these algorithms on test problems with difficult Pareto Set topologies. We focus on separable unimodal and multimodal functions with 2, 3, and 4 objectives, all having difficult Pareto Set topologies. Our experimental study provides some interesting and useful insights to understand better Pareto dominance-based MOEAs.

  • GECCO (Companion) - Pareto dominance-based MOEAs on problems with difficult Pareto Set topologies
    Proceedings of the Genetic and Evolutionary Computation Conference Companion on - GECCO '18, 2018
    Co-Authors: Yuri Marca, Hernan Aguirre, Arnaud Liefooghe, Bilel Derbel, Sebastien Verel, Saul Zapotecas, Kiyoshi Tanaka
    Abstract:

    Despite the extensive application of multi-objective evolutionary algorithms (MOEAs) to solve multi-objective optimization problems (MOPs), understanding their working principles is still open to research. One of the most popular and successful MOEA approaches is based on Pareto dominance and its relaxed version, Pareto ϵ-dominance. However, such approaches have not been sufficiently studied in problems of increased complexity. In this work, we study the effects of the working mechanisms of the various components of these algorithms on test problems with difficult Pareto Set topologies. We focus on separable unimodal and multimodal functions with 2, 3, and 4 objectives, all having difficult Pareto Set topologies. Our experimental study provides some interesting and useful insights to understand better Pareto dominance-based MOEAs.

Arnaud Liefooghe - One of the best experts on this subject based on the ideXlab platform.

  • approximating Pareto Set topology by cubic interpolation on bi objective problems
    International Conference on Evolutionary Multi-criterion Optimization, 2019
    Co-Authors: Yuri Marca, Hernan Aguirre, Saul Zapotecas Martinez, Arnaud Liefooghe, Bilel Derbel, Sebastien Verel, Kiyoshi Tanaka
    Abstract:

    Difficult Pareto Set topology refers to multi-objective problems with geometries of the Pareto Set such that neighboring optimal solutions in objective space differ in several or all variables in decision space. These problems can present a tough challenge for evolutionary multi-objective algorithms to find a good approximation of the optimal Pareto Set well-distributed in decision and objective space. One important challenge optimizing these problems is to keep or restore diversity in decision space. In this work, we propose a method that learns a model of the topology of the solutions in the population by performing parametric spline interpolations for all variables in decision space. We use Catmull-Rom parametric curves as they allow us to deal with any dimension in decision space. The proposed method is appropriated for bi-objective problems since their optimal Set is a one-dimensional curve according to the Karush-Kuhn-Tucker condition. Here, the proposed method is used to promote restarts from solutions generated by the model. We study the effectiveness of the proposed method coupled to NSGA-II and two variations of MOEA/D on problems with difficult Pareto Set topology. These algorithms approach very differently the Pareto Set. We argue and discuss their behavior and its implications for model building.

  • EMO - Approximating Pareto Set Topology by Cubic Interpolation on Bi-objective Problems.
    Lecture Notes in Computer Science, 2019
    Co-Authors: Yuri Marca, Hernan Aguirre, Saul Zapotecas Martinez, Arnaud Liefooghe, Bilel Derbel, Sebastien Verel, Kiyoshi Tanaka
    Abstract:

    Difficult Pareto Set topology refers to multi-objective problems with geometries of the Pareto Set such that neighboring optimal solutions in objective space differ in several or all variables in decision space. These problems can present a tough challenge for evolutionary multi-objective algorithms to find a good approximation of the optimal Pareto Set well-distributed in decision and objective space. One important challenge optimizing these problems is to keep or restore diversity in decision space. In this work, we propose a method that learns a model of the topology of the solutions in the population by performing parametric spline interpolations for all variables in decision space. We use Catmull-Rom parametric curves as they allow us to deal with any dimension in decision space. The proposed method is appropriated for bi-objective problems since their optimal Set is a one-dimensional curve according to the Karush-Kuhn-Tucker condition. Here, the proposed method is used to promote restarts from solutions generated by the model. We study the effectiveness of the proposed method coupled to NSGA-II and two variations of MOEA/D on problems with difficult Pareto Set topology. These algorithms approach very differently the Pareto Set. We argue and discuss their behavior and its implications for model building.

  • Pareto dominance based moeas on problems with difficult Pareto Set topologies
    Genetic and Evolutionary Computation Conference, 2018
    Co-Authors: Yuri Marca, Hernan Aguirre, Arnaud Liefooghe, Bilel Derbel, Sebastien Verel, Saul Zapotecas, Kiyoshi Tanaka
    Abstract:

    Despite the extensive application of multi-objective evolutionary algorithms (MOEAs) to solve multi-objective optimization problems (MOPs), understanding their working principles is still open to research. One of the most popular and successful MOEA approaches is based on Pareto dominance and its relaxed version, Pareto ϵ-dominance. However, such approaches have not been sufficiently studied in problems of increased complexity. In this work, we study the effects of the working mechanisms of the various components of these algorithms on test problems with difficult Pareto Set topologies. We focus on separable unimodal and multimodal functions with 2, 3, and 4 objectives, all having difficult Pareto Set topologies. Our experimental study provides some interesting and useful insights to understand better Pareto dominance-based MOEAs.

  • GECCO (Companion) - Pareto dominance-based MOEAs on problems with difficult Pareto Set topologies
    Proceedings of the Genetic and Evolutionary Computation Conference Companion on - GECCO '18, 2018
    Co-Authors: Yuri Marca, Hernan Aguirre, Arnaud Liefooghe, Bilel Derbel, Sebastien Verel, Saul Zapotecas, Kiyoshi Tanaka
    Abstract:

    Despite the extensive application of multi-objective evolutionary algorithms (MOEAs) to solve multi-objective optimization problems (MOPs), understanding their working principles is still open to research. One of the most popular and successful MOEA approaches is based on Pareto dominance and its relaxed version, Pareto ϵ-dominance. However, such approaches have not been sufficiently studied in problems of increased complexity. In this work, we study the effects of the working mechanisms of the various components of these algorithms on test problems with difficult Pareto Set topologies. We focus on separable unimodal and multimodal functions with 2, 3, and 4 objectives, all having difficult Pareto Set topologies. Our experimental study provides some interesting and useful insights to understand better Pareto dominance-based MOEAs.