Self-adaptation in non-Elitist Evolutionary Algorithms on Discrete Problems with Unknown Structure

04/01/2020
by   Brendan Case, et al.
2

A key challenge to make effective use of evolutionary algorithms is to choose appropriate settings for their parameters. However, the appropriate parameter setting generally depends on the structure of the optimisation problem, which is often unknown to the user. Non-deterministic parameter control mechanisms adjust parameters using information obtained from the evolutionary process. Self-adaptation – where parameter settings are encoded in the chromosomes of individuals and evolve through mutation and crossover – is a popular parameter control mechanism in evolutionary strategies. However, there is little theoretical evidence that self-adaptation is effective, and self-adaptation has largely been ignored by the discrete evolutionary computation community. Here we show through a theoretical runtime analysis that a non-elitist, discrete evolutionary algorithm which self-adapts its mutation rate not only outperforms EAs which use static mutation rates on , but also improves asymptotically on an EA using a state-of-the-art control mechanism. The structure of this problem depends on a parameter k, which is a priori unknown to the algorithm, and which is needed to appropriately set a fixed mutation rate. The self-adaptive EA achieves the same asymptotic runtime as if this parameter was known to the algorithm beforehand, which is an asymptotic speedup for this problem compared to all other EAs previously studied. An experimental study of how the mutation-rates evolve show that they respond adequately to a diverse range of problem structures. These results suggest that self-adaptation should be adopted more broadly as a parameter control mechanism in discrete, non-elitist evolutionary algorithms.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/17/2016

Self-adaptation of Mutation Rates in Non-elitist Populations

The runtime of evolutionary algorithms (EAs) depends critically on their...
research
03/08/2023

Towards Self-adaptive Mutation in Evolutionary Multi-Objective Algorithms

Parameter control has succeeded in accelerating the convergence process ...
research
08/16/2018

The linear hidden subset problem for the (1+1) EA with scheduled and adaptive mutation rates

We study unbiased (1+1) evolutionary algorithms on linear functions with...
research
06/19/2015

Solving Problems with Unknown Solution Length at (Almost) No Extra Cost

Most research in the theory of evolutionary computation assumes that the...
research
09/11/2017

Evolution of Convolutional Highway Networks

Convolutional highways are deep networks based on multiple stacked convo...
research
04/11/2022

Effective Mutation Rate Adaptation through Group Elite Selection

Evolutionary algorithms are sensitive to the mutation rate (MR); no sing...
research
05/16/2014

Leveraging Evolutionary Search to Discover Self-Adaptive and Self-Organizing Cellular Automata

Building self-adaptive and self-organizing (SASO) systems is a challengi...

Please sign up or login with your details

Forgot password? Click here to reset