Self-adaptation of Genetic Operators Through Genetic Programming Techniques

TL;DR

This paper introduces an evolutionary algorithm that dynamically adapts its genetic operators using genetic programming techniques, improving convergence and diversity in solving benchmark functions.

Contribution

It presents a novel method where genetic operators evolve alongside solutions, enhancing adaptability and performance in evolutionary algorithms.

Findings

Improved convergence on benchmark functions

Enhanced diversity in solutions

Effective operator evolution analysis

Abstract

Here we propose an evolutionary algorithm that self modifies its operators at the same time that candidate solutions are evolved. This tackles convergence and lack of diversity issues, leading to better solutions. Operators are represented as trees and are evolved using genetic programming (GP) techniques. The proposed approach is tested with real benchmark functions and an analysis of operator evolution is provided.