3 research outputs found
Genetic Transfer or Population Diversification? Deciphering the Secret Ingredients of Evolutionary Multitask Optimization
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.Comment: 7 pages, 6 figure
Multifactorial Evolutionary Algorithm For Clustered Minimum Routing Cost Problem
Minimum Routing Cost Clustered Tree Problem (CluMRCT) is applied in various
fields in both theory and application. Because the CluMRCT is NP-Hard, the
approximate approaches are suitable to find the solution for this problem.
Recently, Multifactorial Evolutionary Algorithm (MFEA) has emerged as one of
the most efficient approximation algorithms to deal with many different kinds
of problems. Therefore, this paper studies to apply MFEA for solving CluMRCT
problems. In the proposed MFEA, we focus on crossover and mutation operators
which create a valid solution of CluMRCT problem in two levels: first level
constructs spanning trees for graphs in clusters while the second level builds
a spanning tree for connecting among clusters. To reduce the consuming
resources, we will also introduce a new method of calculating the cost of
CluMRCT solution. The proposed algorithm is experimented on numerous types of
datasets. The experimental results demonstrate the effectiveness of the
proposed algorithm, partially on large instance