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Evolutionary multitasking has recently emerged as a novel paradigm thatenables the similarities and/or latent complementarities (if present) betweendistinct optimization tasks to be exploited in an autonomous manner simply bysolving them together with a unified solution representation scheme. Animportant matter underpinning future algorithmic advancements is to develop abetter understanding of the driving force behind successful multitaskproblem-solving. In this regard, two (seemingly disparate) ideas have been putforward, namely, (a) implicit genetic transfer as the key ingredientfacilitating the exchange of high-quality genetic material across tasks, and(b) population diversification resulting in effective global search of theunified search space encompassing all tasks. In this paper, we present someempirical results that provide a clearer picture of the relationship betweenthe two aforementioned propositions. For the numerical experiments we make useof Sudoku puzzles as case studies, mainly because of their feature thatoutwardly unlike puzzle statements can often have nearly identical finalsolutions. The experiments reveal that while on many occasions genetic transferand population diversity may be viewed as two sides of the same coin, the widerimplication of genetic transfer, as shall be shown herein, captures the trueessence of evolutionary multitasking to the fullest.

Genetic Transfer or Population Diversification? Deciphering the Secret Ingredients of Evolutionary Multitask Optimization