Automated Configuration of Genetic Algorithms by Tuning for Anytime Performance

06/11/2021
by   Furong Ye, et al.
13

Finding the best configuration of algorithms' hyperparameters for a given optimization problem is an important task in evolutionary computation. We compare in this work the results of four different hyperparameter tuning approaches for a family of genetic algorithms on 25 diverse pseudo-Boolean optimization problems. More precisely, we compare previously obtained results from a grid search with those obtained from three automated configuration techniques: iterated racing, mixed-integer parallel efficient global optimization, and mixed-integer evolutionary strategies. Using two different cost metrics, expected running time and the area under the empirical cumulative distribution function curve, we find that in several cases the best configurations with respect to expected running time are obtained when using the area under the empirical cumulative distribution function curve as the cost metric during the configuration process. Our results suggest that even when interested in expected running time performance, it might be preferable to use anytime performance measures for the configuration task. We also observe that tuning for expected running time is much more sensitive with respect to the budget that is allocated to the target algorithms.

READ FULL TEXT

page 1

page 6

page 7

page 9

page 10

page 11

12/12/2019

Sequential vs. Integrated Algorithm Selection and Configuration: A Case Study for the Modular CMA-ES

When faced with a specific optimization problem, choosing which algorith...
02/12/2021

Leveraging Benchmarking Data for Informed One-Shot Dynamic Algorithm Selection

A key challenge in the application of evolutionary algorithms in practic...
06/17/2019

Running Time Analysis of the (1+1)-EA for Robust Linear Optimization

Evolutionary algorithms (EAs) have found many successful real-world appl...
08/04/2022

MAGPIE: Machine Automated General Performance Improvement via Evolution of Software

Performance is one of the most important qualities of software. Several ...
07/03/2019

Optimal Decision Trees for the Algorithm Selection Problem: Integer Programming Based Approaches

Even though it is well known that for most relevant computational proble...
06/15/2012

General Upper Bounds on the Running Time of Parallel Evolutionary Algorithms

We present a new method for analyzing the running time of parallel evolu...
09/04/2019

A Specialized Evolutionary Strategy Using Mean Absolute Error Random Sampling to Design Recurrent Neural Networks

Recurrent neural networks have demonstrated to be good at solving predic...