Speeding-up Evolutionary Algorithms to solve Black-Box Optimization Problems

TL;DR

This paper introduces a method to dynamically select the most cost-effective approximation of the objective function during evolutionary algorithm runs, significantly reducing evaluation time while maintaining solution quality.

Contribution

It proposes a technique to adaptively choose the approximate function cost during optimization, balancing evaluation cost and accuracy for efficient black-box problem solving.

Findings

Achieves similar solutions in less than half the time on various problems.

Reduces evaluation costs without compromising solution quality.

Demonstrates effectiveness across diverse black-box optimization scenarios.

Abstract

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.