Genetic Transfer or Population Diversification? Deciphering the Secret Ingredients of Evolutionary Multitask Optimization
📝 Abstract
Evolutionary multitasking has recently emerged as a novel paradigm that enables the similarities and/or latent complementarities (if present) between distinct optimization tasks to be exploited in an autonomous manner simply by solving them together with a unified solution representation scheme. An important matter underpinning future algorithmic advancements is to develop a better understanding of the driving force behind successful multitask problem-solving. In this regard, two (seemingly disparate) ideas have been put forward, namely, (a) implicit genetic transfer as the key ingredient facilitating the exchange of high-quality genetic material across tasks, and (b) population diversification resulting in effective global search of the unified search space encompassing all tasks. In this paper, we present some empirical results that provide a clearer picture of the relationship between the two aforementioned propositions. For the numerical experiments we make use of Sudoku puzzles as case studies, mainly because of their feature that outwardly unlike puzzle statements can often have nearly identical final solutions. The experiments reveal that while on many occasions genetic transfer and population diversity may be viewed as two sides of the same coin, the wider implication of genetic transfer, as shall be shown herein, captures the true essence of evolutionary multitasking to the fullest.
💡 Analysis
Evolutionary multitasking has recently emerged as a novel paradigm that enables the similarities and/or latent complementarities (if present) between distinct optimization tasks to be exploited in an autonomous manner simply by solving them together with a unified solution representation scheme. An important matter underpinning future algorithmic advancements is to develop a better understanding of the driving force behind successful multitask problem-solving. In this regard, two (seemingly disparate) ideas have been put forward, namely, (a) implicit genetic transfer as the key ingredient facilitating the exchange of high-quality genetic material across tasks, and (b) population diversification resulting in effective global search of the unified search space encompassing all tasks. In this paper, we present some empirical results that provide a clearer picture of the relationship between the two aforementioned propositions. For the numerical experiments we make use of Sudoku puzzles as case studies, mainly because of their feature that outwardly unlike puzzle statements can often have nearly identical final solutions. The experiments reveal that while on many occasions genetic transfer and population diversity may be viewed as two sides of the same coin, the wider implication of genetic transfer, as shall be shown herein, captures the true essence of evolutionary multitasking to the fullest.
📄 Content
Abstract — Evolutionary multitasking has recently emerged as a novel paradigm that enables the similarities and/or latent complementarities (if present) between distinct optimization tasks to be exploited in an autonomous manner simply by solving them together with a unified solution representation scheme. An important matter underpinning future algorithmic advancements is to develop a better understanding of the driving force behind successful multitask problem-solving. In this regard, two (seemingly disparate) ideas have been put forward, namely, (a) implicit genetic transfer as the key ingredient facilitating the exchange of high-quality genetic material across tasks, and (b) population diversification resulting in effective global search of the unified search space encompassing all tasks. In this paper, we present some empirical results that provide a clearer picture of the relationship between the two aforementioned propositions. For the numerical experiments we make use of Sudoku puzzles as case studies, mainly because of their feature that outwardly unlike puzzle statements can often have nearly identical final solutions. The experiments reveal that while on many occasions “genetic transfer” and “population diversity” may be viewed as two sides of the same coin, the wider implication of genetic transfer, as shall be shown herein, captures the true essence of evolutionary multitasking to the fullest.
Index Terms —Evolutionary Multitasking, Genetic Transfer,
Diversity, Sudoku.
I. INTRODUCTION
In today’s world of rapidly increasing volume, speed, and
complexity of real-world challenges, the ability to effectively
multitask, both cognitively as well as on computational
platforms, is gaining much importance with the potential for
productivity enhancement acting as the key motivation [1].
While some notable success stories can be found in the field of
machine learning [2], much is yet to be explored in the context
of numerical optimization and the biologically inspired search
algorithms of computational intelligence. In this regard, recent
works towards enhancing the population-based search
algorithms of evolutionary computation (EC) for tackling
multiple optimization tasks at once
with a unified
representation scheme, have shown considerable promise [3],
[4]. In fact, it is noted that the implicit parallelism of a
population is naturally well suited for the purpose of
multitasking, providing the scope for spontaneous online
sharing of knowledge building-blocks (in the form of encoded
genetic material) across tasks.
In order to further emphasize the practical significance of
the above, we consider the extreme case of two minimization
tasks T1 and T2 characterized by cost functions f1 and f2 of high
ordinal correlation [5]. In other words, for any pair of points
y1 and y2 in a unified search space Y, f1(y1) < f1(y2) ⇔ f2(y1) <
f2(y2). Thus, on solving the two tasks together via
multitasking, any series of search steps leading to a cost
reduction of T1 automatically leads to a cost reduction of T2
for free, and vice versa, without the need for added function
evaluations. As a result, it is clear that at least within the
family of functions with high ordinal correlation, the idea of
evolutionary multitasking (or multitask optimization) gives
rise to free lunches [6], [7].
Recently in [8], a novel multifactorial evolutionary
algorithm (MFEA) has been proposed as a means of
exploiting the relationship between optimization tasks via the
process of multitasking. The algorithm’s nomenclature follows
from the fact that each task is viewed as a unique factor
influencing the evolution of a single population of individuals
(artificial search agents). The algorithm provides a cross-
domain optimization platform and has been tested on a variety
of benchmarks as well as real-world instances, involving
continuous and discrete problems, often leading to noteworthy
results (a summary can be found in [1]).
The effectiveness of evolutionary multitasking showcased
so far in the preliminary development stage has raised some
interesting questions that are expected to direct future
algorithmic advancements. In particular, it is considered
critical to decipher the key ingredients driving the success of
evolutionary multitasking. In this regard, two seemingly
unrelated ideas have been put forward, namely, (a) implicit
transfer of high-quality genetic building-blocks across tasks,
and (b) population diversity leading to better global search.
It is noted that the boon of population diversification has
been strongly emphasized in the design of island models of
parallel genetic algorithms for single-tasking [9], [10], which
may be seen to have some connections with the multitasking
MFEA due to the implicit creation of subpopulations [8]. As a
result, there is a tendency to assign the credit of MFEA’s
success merely to the exploitation of population
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