Speeding-up Evolutionary Algorithms to solve Black-Box Optimization Problems
CoRR(2023)
摘要
Population-based evolutionary algorithms are often considered when
approaching computationally expensive black-box optimization problems. They
employ a selection mechanism to choose the best solutions from a given
population after comparing their objective values, which are then used to
generate the next population. This iterative process explores the solution
space efficiently, leading to improved solutions over time. However, these
algorithms require a large number of evaluations to provide a quality solution,
which might be computationally expensive when the evaluation cost is high. In
some cases, it is possible to replace the original objective function with a
less accurate approximation of lower cost. This introduces a trade-off between
the evaluation cost and its accuracy.
In this paper, we propose a technique capable of choosing an appropriate
approximate function cost during the execution of the optimization algorithm.
The proposal finds the minimum evaluation cost at which the solutions are still
properly ranked, and consequently, more evaluations can be computed in the same
amount of time with minimal accuracy loss. An experimental section on four very
different problems reveals that the proposed approach can reach the same
objective value in less than half of the time in certain cases.
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关键词
population-based evolutionary algorithm,computationally expensive black-box problem,evaluation cost,approximate objective function
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