The paradigm of Wang’s paper is computational biology, more specifically genome rearrangement. The genome rearrangement problem simply means changing one genome sequence to another in the shortest possible way.

There are several genome rearrangement problems in the literature. One that is widely studied is the all sorting reversal (ASR) problem, which is all about finding the shortest chain of reversals (evolutionary changes in species) that converts one chromosome into another.

For the ASR problem, Seipel proposed an algorithm that executes in cubic time complexity with bad components (which are certain typical structures). But bad components being unrealistic (God is not an antagonist!), Swenson and others improved it with an algorithm with quadratic runtime complexity. However, it has been recently observed that this improved algorithm does not work for all inputs. This motivated the author to build a more robust algorithm with the same quadratic worst-case complexity. Wang’s algorithm is linear with respect to input and output size. Since algorithms for detecting the bad components are available, it may be possible to combine Wang’s algorithm with Seipel’s algorithm to solve the general ASR problem in quadratic time in the average case. The paper is useful and will certainly draw some interest among researchers and postgraduates in computing science.