Multi-Objective Optimization Approaches in Biological Learning System on Microarray Data

Multi-Objective optimization is a well-known and efficient method in computer science. In various real-life problems, multiple conflicting objective functions need to be optimized simultaneously to attain the desired goal of the underlying pattern recognition task. The approaches of multi-objective optimization have a great impact in designing sophisticated learning systems, especially building robust biological learning systems. Remembering that, in this book chapter, we provide a comprehensive review of various multi-objective optimization techniques used in biological learning systems dealing with the microarray or RNA-Seq data. In this regard, the task of designing a multi-class cancer classification system employing a multi-objective optimization technique is first addressed. Next, how a gene regulatory network can be built from a perspective of multi-objective optimization is discussed. The next application deals with fuzzy clustering of categorical attributes using a multi-objective genetic algorithm. After this, how microarray data can be automatically clustered using a multi-objective differential evolution is addressed. Then, the applicability of multi-objective particle swarm optimization techniques in identifying gene markers is explored. The next application concentrates on feature selection for microarray data using a multi-objective binary particle swarm optimization technique. Thereafter, a multi-objective optimization approach is addressed for producing differentially coexpressed module during the progression of the HIV disease. In addition, we represent a comparative study based on the literature along with highlighting the advantages and limitations of the methods. Finally, our study depicts a new direction to bioinspired learning system related to multi-objective optimization.