A Crossover That Matches Diverse Parents Together in Evolutionary Algorithms

TL;DR

This paper introduces a novel crossover method for evolutionary decision tree construction that emphasizes diverse parent pairing based on complementary fitness, improving solution quality in certain variants.

Contribution

The paper proposes a new crossover technique that selects diverse, complementary parents using a calculated fitness measure, enhancing evolutionary algorithm performance.

Findings

One variant of the proposed method outperforms the baseline.

Other variants underperform compared to the baseline.

The method's efficacy varies across different configurations.

Abstract

Crossover and mutation are the two main operators that lead to new solutions in evolutionary approaches. In this article, a new method of performing the crossover phase is presented. The problem of choice is evolutionary decision tree construction. The method aims at finding such individuals that together complement each other. Hence we say that they are diversely specialized. We propose the way of calculating the so-called complementary fitness. In several empirical experiments, we evaluate the efficacy of the method proposed in four variants and compare it to a fitness-rank-based approach. One variant emerges clearly as the best approach, whereas the remaining ones are below the baseline.