A Survey of League Championship Algorithm: Prospects and Challenges
The League Championship Algorithm (LCA) is sport-inspired optimization algorithm that was introduced by Ali Husseinzadeh Kashan in the year 2009. It has since drawn enormous interest among the researchers because of its potential efficiency in solving many optimization problems and real-world applications. The LCA has also shown great potentials in solving non-deterministic polynomial time (NP-complete) problems. This survey presents a brief synopsis of the LCA literatures in peer-reviewed journals, conferences and book chapters. These research articles are then categorized according to indexing in the major academic databases (Web of Science, Scopus, IEEE Xplore and the Google Scholar). The analysis was also done to explore the prospects and the challenges of the algorithm and its acceptability among researchers. This systematic categorization can be used as a basis for future studies.
Code (0)
등록된 구현이 없습니다.
Tasks
ArticlesSurveySimilar Papers 제목 키워드 기반
BetaRun Soccer Simulation League Team: Variety, Complexity, and Learning
RoboCup offers a set of benchmark problems for Artificial Intelligence in form of official world championships since 1997. The most tactical advanced and richest in terms of behavioural complexity of these is the 2D Socc…
BIG-bench Machine LearningTrouble with the Curve: Predicting Future MLB Players Using Scouting Reports
In baseball, a scouting report profiles a player's characteristics and traits, usually intended for use in player valuation. This work presents a first-of-its-kind dataset of almost 10,000 scouting reports for minor leag…
ArticlesLuck is Hard to Beat: The Difficulty of Sports Prediction
Predicting the outcome of sports events is a hard task. We quantify this difficulty with a coefficient that measures the distance between the observed final results of sports leagues and idealized perfectly balanced comp…
Video Highlight Prediction Using Audience Chat Reactions
Sports channel video portals offer an exciting domain for research on multimodal, multilingual analysis. We present methods addressing the problem of automatic video highlight prediction based on joint visual features an…
PredictionRecent Advances on Non-Line-of-Sight Imaging: Conventional Physical Models, Deep Learning, and New Scenes
As an emerging technology that has attracted huge attention, non-line-of-sight (NLOS) imaging can reconstruct hidden objects by analyzing the diffuse reflection on a relay surface, with broad application prospects in the…
Autonomous DrivingDeep LearningSurvey