Surrogate-assisted Evolutionary Algorithm

JPNSEC Symposium on Evolutionary Computation 2024

Notice All materials on this page are author’s versions, not necessarily coincide with final published versions.

I gave a poster presentation at International Conference on Parallel Problem Solving from Nature 2024 (PPSN 2024, University of Applied Sciences Upper Austria, Hagenberg im Mühlkreis, Austria, 14-18, Sep). All accepted papers were presented as poster presentations in PPSN. Thank you to all those who helped make this event possible.

International Conference on Parallel Problem Solving from Nature 2024 (PPSN 2024)

This work proposes a new surrogate-assisted evolutionary algorithm that partially optimizes each objective/constraint, namely surrogate-assisted partial optimization (SAPO). Solutions with better values of objective/constraint are selected from the evaluated solutions as the parent solutions and a focused objective/constraint is independently optimized using surrogate models one by one.

This paper presents a novel surrogate-assisted evolutionary algorithm based on scalarization function approximation, which is designed to strengthen its robustness against this deterioration. The proposed algorithm, called SFA/DE, constructs an approximation model for each scalarization function defined in a decomposition-based framework. Each decomposed problem is then solved using multiple independent models trained for its neighbor problems.

I gave a presentation at JPNSEC Symposium on Evolutionary Computation 2023 (Ohoribata Convention Hall, Odawara, 21-23, Dec.). I was also a student member of organizers for the Open Space Discussion. Thank you to all those who helped make this event possible.

Notice All materials on this page are author’s versions, not necessarily coincide with final published versions.

Notice All materials on this page are author’s versions, not necessarily coincide with final published versions.

JPNSEC Symposium on Evolutionary Computation 2023