Seeding the Initial Population of Multi-Objective Evolutionary Algorithms: A Computational Study
This work addresses a gap in optimizing multi-objective problems for researchers and practitioners, but it is incremental as it extends known seeding techniques from single-objective to multi-objective contexts.
The study investigated the impact of seeding initial populations in multi-objective evolutionary algorithms on real-valued test functions, finding that benefits vary significantly across functions and algorithms, with some like DTLZ4 showing substantial gains while others like WFG profit less.
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.