337 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Chapter 14 DOI: 10.4018/978-1-5225-2607-0.ch014 ABSTRACT Pathway information for cancer detection helps to fnd co-regulated gene groups whose collective expres- sion is strongly associated with cancer development. In this paper, a collaborative multi-swarm binary particle swarm optimization (MS-BPSO) based gene selection technique is proposed that outperforms to identify the pathway marker genes. We have compared our proposed method with various statistical and pathway based gene selection techniques for diferent popular cancer datasets as well as a detailed comparative study is illustrated using diferent meta-heuristic algorithms like binary coded particle swarm optimization (BPSO), binary coded diferential evolution (BDE), binary coded artifcial bee colony (BABC) and genetic algorithm (GA). Experimental results show that the proposed MS-BPSO based method performs signifcantly better and the improved multi swarm concept generates a good subset of pathway markers which provides more efective insight to the gene-disease association with high accuracy and reliability. INTRODUCTION Genes control the different functioning of a cell, like growth, division, death etc. When the normal profile of a gene is changed or damaged, it causes the abnormal behavior of the cell and we, in generic sense, call it as cancer. Cancer is nothing but out-of-control cell growth due to change in the expression profile of genes. Advancement of microarray technology has made the genomic study more fast and Selection of Pathway Markers for Cancer Using Collaborative Binary Multi- Swarm Optimization Prativa Agarwalla Heritage Institute of Technology, India Sumitra Mukhopadhyay Institute of Radiophysics and Electronics, India