Comments on Research Work

2001. Drift analysis and average time complexity of evolutionary algorithms

1. "The drift theorem due to He and Yao, which goes back to Hajek, provides a general technique for proving exponential lower bounds on the first hitting-time in such Markov processes.'' (Giel and Lehre 2010)

2. "we apply drift analysis arguments presented by He and Yao'' (Jansen and Sudholt 2010)

3. "Drift analysis, which is widely used by now, has been put forward by He and Yao.'' (Jagersküpper 2011).

4. "Drift analysis is a powerful tool used to bound the optimization time of evolutionary algorithms (EAs)." "Recently drift analysis, a technique that goes back to the 1940s, was introduced for the analysis of EAs by He and Yao.'' (Oliveto and Witt 2011)

5. "Drift analysis was introduced to the theory of evolutionary algorithms by He and Yao. It soon became one of the strongest tools both for proving run-time guarantees for many evolutionary algorithms and for giving evidence that some algorithms cannot solve certain problems.'' (Doerr, Johannsen and Winzen 2012)

6. "drift analysis, a method that provided important insights into the computational complexity of discrete EAs over the last decade.'' (Agapie, Agapie, Rudolph and Zbaganu 2013)

7. "Expected runtime analysis inspects the average runtime of an algorithm on a particular problem, and can exploit mature probabilistic techniques, such as drift analysis and others.'' (Wu, Kolonko and Moehring 2017)

8. "drift analysis, a standard tool in theory of randomised search heuristics" (Corus, Dang, Eremeev and Lehre, 2017)

9. "we use the following lemma to bound the expected absorption time of a supermartingale. The techniques involved are well-known; see Hajek [14] or He and Yao [15]." (Goldberg, Lapinskas and Richerby 2020)

2015. On the easiest and hardest fitness functions

1. "the theory in Ref. [12] that indicates easy problems for one algorithm may be difficult for another, and vice versa;" (Lou and Yuen 2019)

2. "Recently, He et al. [14] also presented the easiest and hardest fitness functions for RSHs, and revealed that the unimodal functions are the easiest and deceptive functions are the hardest in terms of the time-fitness landscape. This further explains that RSHs are not always efficient for all problems." (Xia and Zhou 2018)

2016. Average convergence rate of evolutionary algorithms

1. "The fourth type of convergence representation is the average convergence rate that specifically measures the rate of fitness change as proposed by He and Lin (2016)." (Halim, Ismail and Das, 2020)

2. "Convergence ratio can be evaluated by Markow chain analysis, however, this process is complicated from theoretical and practical point of view. For this reason, the convergence indicator is described as (He and Lin, 2016)". (Janiga, Czarnota, Stopa and Wojnarowski, 2019)

2019. A novel two-archive strategy for evolutionary many-objective optimization algorithm based on reference points

1. "For example, the two-archive techniques that establish repositories to maintain convergence and diversity performance of the approximate Pareto solution have been verified and upgraded considering high-dimensional spaces [42]'" (Pan et al 2020)

2. "Different from the existing double level archives strategy in [23], the principle of the double-archive mechanism can be seen from Fig.3." (Guan et al 2020)

3. "The convergence and diversity of solutions were guaranteed by two archives respectively in NSGA-III-UE and achieved good results [34]." (Gu et al. 2021)