Seeding the Initial Population of Multi-Objective Evolutionary Algorithms: A Computational Study

TL;DR

This study systematically evaluates how different seeding techniques influence the performance of multi-objective evolutionary algorithms across various test functions and objectives.

Contribution

It provides a comprehensive computational analysis of seeding methods' effects on multiple algorithms and test problems in multi-objective optimization.

Findings

Seeding benefits vary significantly across different functions.

Some functions like DTLZ4 and LZ benefit greatly from seeding.

The effectiveness of seeding depends on the specific algorithm used.

Abstract

Most experimental studies initialize the population of evolutionary algorithms with random genotypes. In practice, however, optimizers are typically seeded with good candidate solutions either previously known or created according to some problem-specific method. This "seeding" has been studied extensively for single-objective problems. For multi-objective problems, however, very little literature is available on the approaches to seeding and their individual benefits and disadvantages. In this article, we are trying to narrow this gap via a comprehensive computational study on common real-valued test functions. We investigate the effect of two seeding techniques for five algorithms on 48 optimization problems with 2, 3, 4, 6, and 8 objectives. We observe that some functions (e.g., DTLZ4 and the LZ family) benefit significantly from seeding, while others (e.g., WFG) profit less. The advantage of seeding also depends on the examined algorithm.