Heterogeneous Parallel Genetic Algorithm Paradigm
This addresses a foundational issue in genetic algorithms for researchers and practitioners, but it appears incremental as it builds on existing paradigms without a major breakthrough.
The paper tackles the problem of genetic algorithm performance being hindered by encoding representation, proposing a heterogeneous parallel genetic algorithm that federates multiple encoding representations to solve problems efficiently without prior knowledge of the best encoding, though no concrete results or numbers are provided.
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.