Performance Analysis of Nature‐Inspired Algorithms in Breast Cancer Diagnosis

Nature-inspired computing (NIC) is a fascinating computing paradigm that
applies the methodology and approaches of nature that addresses various realtime
complex problems ranging from how an organism finds its prey to genetic
evolution. One of the unique features is that it has been provisioned with a decentralized
control of computational activities naturally. In this chapter, to have a better
insight of such NIC-based algorithms, the problem of identifying the breast
cancer is used to exhibit their performances. Also, this chapter briefs about the
application of stand-alone and hybridized approaches to identify the disease.
Finally, it concludes with the experimental results and other statistical measures.
The purpose of the chapter is to guide the new researcher in the area to get inspired
from the conventional works and to bring out a new advanced approach that can
perform further better. The three swarm algorithms are ant colony optimization,
firefly, and particle swarm optimization algorithm. These swarm algorithms were
used to optimize the support vector machine (SVM) which was trained to classify
the malignant and benign images from the Wisconsin breast cancer dataset. With
respect to the experimental results, it has been found that naïve algorithm with PSO optimization demonstrates better discriminating property of the underlying
conventional classifiers.