Abstract: Data mining is the process of extracting useful information from this flooded data, which helps in making profitable future decisions in these fields. Evolutionary algorithms (EA) are generic meta-heuristic optimization algorithms that use techniques inspired by nature’s evolutionary processes. EA maintains a whole set of solutions that are optimized at the same time instead of a one single solution. The inherent randomness of the emulated biological processes enables them to provide good approximate solutions nevertheless. The recently emerged nature-inspired multi-objective meta-heuristic optimization algorithms Teaching Learning-Based Optimization (TLBO) and its variations Elitist TLBO belong to this category. Both these algorithms aim to find global solutions for real world problem with less computational effort and high reliability. The principle idea behind TLBO is the simulation of teaching–learning process of a traditional classroom in to algorithmic representation with two phases called teaching and learning. Elitist TLBO was pioneered with a major modification to eliminate the duplicate solutions in learning phase. In this paper, we have studied about different-different efficient algorithm of TLBO that was designed. Also, a brief study has given about TLBO phases and comparison.
Keywords: Data Mining, TLBO, Elitist TLBO, TLBO phases, Elitist TLBO algorithms
|
DOI:
10.17148/IJARCCE.2019.8205
