Multiobjective Evolutionary Component Effect on Algorithm behavior
- URL: http://arxiv.org/abs/2308.02527v1
- Date: Mon, 31 Jul 2023 16:02:56 GMT
- Title: Multiobjective Evolutionary Component Effect on Algorithm behavior
- Authors: Yuri Lavinas, Marcelo Ladeira, Gabriela Ochoa, Claus Aranha
- Abstract summary: It is unknown what are the most influential components that lead to performance improvements.
We apply this methodology to a tuned Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D) designed by the iterated racing (irace) configuration package.
We compare the impact of the algorithm components in terms of their Search Trajectory Networks (STNs), the diversity of the population, and the anytime hypervolume values.
- Score: 0.04588028371034406
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The performance of multiobjective evolutionary algorithms (MOEAs) varies
across problems, making it hard to develop new algorithms or apply existing
ones to new problems. To simplify the development and application of new
multiobjective algorithms, there has been an increasing interest in their
automatic design from their components. These automatically designed
metaheuristics can outperform their human-developed counterparts. However, it
is still unknown what are the most influential components that lead to
performance improvements. This study specifies a new methodology to investigate
the effects of the final configuration of an automatically designed algorithm.
We apply this methodology to a tuned Multiobjective Evolutionary Algorithm
based on Decomposition (MOEA/D) designed by the iterated racing (irace)
configuration package on constrained problems of 3 groups: (1) analytical
real-world problems, (2) analytical artificial problems and (3) simulated
real-world. We then compare the impact of the algorithm components in terms of
their Search Trajectory Networks (STNs), the diversity of the population, and
the anytime hypervolume values. Looking at the objective space behavior, the
MOEAs studied converged before half of the search to generally good HV values
in the analytical artificial problems and the analytical real-world problems.
For the simulated problems, the HV values are still improving at the end of the
run. In terms of decision space behavior, we see a diverse set of the
trajectories of the STNs in the analytical artificial problems. These
trajectories are more similar and frequently reach optimal solutions in the
other problems.
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