266 Copyright © 2016, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Chapter 11 DOI: 10.4018/978-1-4666-9964-9.ch011 ABSTRACT Community detection is a topic of great interest in complex network analysis. The basic problem is to identify closely connected groups of nodes (i.e. the communities) from the networks of various objects represented in the form of a graph. Often, the problem is expressed as an optimization problem, where popular optimization techniques such as evolutionary computation techniques are utilized. The importance of these approaches is increasing for efcient community detection with the rapidly growing networks. The primary focus of this chapter is to study the applicability of such techniques for community detection. Our study includes the utilization of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) with their numerous variants developed specifcally for community detection. We have discussed several issues related to community detection, GA, PSO and the major hurdles faced during the implication of evolutionary approaches. In addition, the chapter also includes a detailed study of how these issues are being tackled with the various developments happening in the domain. 1. INTRODUCTION Nowadays, representation of links among various objects present within the data in the form of network is very common in many domains. Such network representation in modern-day data which include so- cial network (Scott, 2012), ecological network (Newman, 2012), biological network (Sah et. al., 2014), Evolutionary Computation Techniques for Community Detection in Social Network Analysis Abhishek Garg Indian Institute of Technology (BHU) Varanasi, India Anupam Biswas Indian Institute of Technology (BHU) Varanasi, India Bhaskar Biswas Indian Institute of Technology (BHU) Varanasi, India