# Heterogeneous Parallel Genetic Algorithm Paradigm

## TL;DR

This paper introduces a heterogeneous parallel genetic algorithm that combines multiple encoding representations to adaptively solve problems efficiently without prior knowledge of the optimal encoding scheme.

## Contribution

It proposes a novel paradigm that federates various encoding schemes in a parallel framework, addressing the challenge of selecting the best encoding for different problem instances.

## Key findings

- Enhances genetic algorithm performance through heterogeneous encoding
- Reduces the need for problem-specific encoding design
- Demonstrates improved adaptability across problem instances

## Abstract

The encoding representation of the genetic algorithm can boost or hinder its performance albeit the care one can devote to operator design. Unfortunately, a representation-theory foundation that helps to find the suitable encoding for any problem has not yet become mature. Furthermore, we argue that such a best-performing encoding scheme can differ even for instances of the same problem. In this contribution, we present the basic principles of the heterogeneous parallel genetic algorithm that federates the efforts of many encoding representations in order to efficiently solve the problem in hand without prior knowledge of the best encoding.