Credit Assignment in Adaptive Evolutionary Algorithms

TL;DR

This paper introduces a novel credit assignment method for search operators in adaptive evolutionary algorithms, improving their performance by better linking operators to successful solutions through a new performance measurement framework.

Contribution

It presents a new framework for credit assignment in evolutionary algorithms, enhancing operator selection based on historical solution success and outperforming existing methods.

Findings

Outperforms various adaptive and non-adaptive algorithms

Introduces a novel framework for performance measurement

Demonstrates improved search bias optimization

Abstract

In this paper, a new method for assigning credit to search operators is presented. Starting with the principle of optimizing search bias, search operators are selected based on an ability to create solutions that are historically linked to future generations. Using a novel framework for defining performance measurements, distributing credit for performance, and the statistical interpretation of this credit, a new adaptive method is developed and shown to outperform a variety of adaptive and non-adaptive competitors.