Many real-world applications have the time-linkage property, and the only theoretical analysis is recently given by Zheng, et al. (TEVC 2021) on their proposed time-linkage OneMax problem, OneMax$_{(0,1^n)}$. However, only two elitist algorithms (1+1
)EA and ($mu$+1)EA are analyzed, and it is unknown whether the non-elitism mechanism could help to escape the local optima existed in OneMax$_{(0,1^n)}$. In general, there are few theoretical results on the benefits of the non-elitism in evolutionary algorithms. In this work, we analyze on the influence of the non-elitism via comparing the performance of the elitist (1+$lambda$)EA and its non-elitist counterpart (1,$lambda$)EA. We prove that with probability $1-o(1)$ (1+$lambda$)EA will get stuck in the local optima and cannot find the global optimum, but with probability $1$, (1,$lambda$)EA can reach the global optimum and its expected runtime is $O(n^{3+c}log n)$ with $lambda=c log_{frac{e}{e-1}} n$ for the constant $cge 1$. Noting that a smaller offspring size is helpful for escaping from the local optima, we further resort to the compact genetic algorithm where only two individuals are sampled to update the probabilistic model, and prove its expected runtime of $O(n^3log n)$. Our computational experiments also verify the efficiency of the two non-elitist algorithms.
Obesity and being over-weight add to the risk of some major life threatening diseases. According to W.H.O., a considerable population suffers from these disease whereas poor nutrition plays an important role in this context. Traditional food activity
monitoring systems like Food Diaries allow manual record keeping of eating activities over time, and conduct nutrition analysis. However, these systems are prone to the problems of manual record keeping and biased-reporting. Therefore, recently, the research community has focused on designing automatic food monitoring systems since the last decade which consist of one or multiple wearable sensors. These systems aim at providing different macro and micro activity detections like chewing, swallowing, eating episodes, and food types as well as estimations like food mass and eating duration. Researchers have emphasized on high detection accuracy, low estimation errors, un-intrusive nature, low cost and real life implementation while designing these systems, however a comprehensive automatic food monitoring system has yet not been developed. Moreover, according to the best of our knowledge, there is no comprehensive survey in this field that delineates the automatic food monitoring paradigm, covers a handful number of research studies, analyses these studies against food intake monitoring tasks using various parameters, enlists the limitations and sets up future directions. In this research work, we delineate the automatic food intake monitoring paradigm and present a survey of research studies. With special focus on studies with wearable sensors, we analyze these studies against food activity monitoring tasks. We provide brief comparison of these studies along with shortcomings based upon experimentation results conducted under these studies. We setup future directions at the end to facilitate the researchers working in this domain.
Search engines can quickly response a hyperlink list according to query keywords. However, when a query is complex, developers need to repeatedly refine the search keywords and open a large number of web pages to find and summarize answers. Many rese
arch works of question and answering (Q and A) system attempt to assist search engines by providing simple, accurate and understandable answers. However, without original semantic contexts, these answers lack explainability, making them difficult for users to trust and adopt. In this paper, a brain-inspired search engine assistant named DeveloperBot based on knowledge graph is proposed, which aligns to the cognitive process of human and has the capacity to answer complex queries with explainability. Specifically, DeveloperBot firstly constructs a multi-layer query graph by splitting a complex multi-constraint query into several ordered constraints. Then it models the constraint reasoning process as subgraph search process inspired by the spreading activation model of cognitive science. In the end, novel features of the subgraph will be extracted for decision-making. The corresponding reasoning subgraph and answer confidence will be derived as explanations. The results of the decision-making demonstrate that DeveloperBot can estimate the answers and answer confidences with high accuracy. We implement a prototype and conduct a user study to evaluate whether and how the direct answers and the explanations provided by DeveloperBot can assist developers information needs.
In real-world applications, many optimization problems have the time-linkage property, that is, the objective function value relies on the current solution as well as the historical solutions. Although the rigorous theoretical analysis on evolutionar
y algorithms has rapidly developed in recent two decades, it remains an open problem to theoretically understand the behaviors of evolutionary algorithms on time-linkage problems. This paper takes the first step to rigorously analyze evolutionary algorithms for time-linkage functions. Based on the basic OneMax function, we propose a time-linkage function where the first bit value of the last time step is integrated but has a different preference from the current first bit. We prove that with probability $1-o(1)$, randomized local search and $(1+1)$ EA cannot find the optimum, and with probability $1-o(1)$, $(mu+1)$ EA is able to reach the optimum.
