A COMPARATIVE STUDY OF CSA & GA BY MULTI OPTIMIZATION

Abstract

In the last few years we perceived a great increase in interest in studying biologically inspired systems. Among these, we can emphasize artificial neural networks, evolutionary computation, DNA computation, and now artificial immune systems. This work discusses immune algorithm and genetic algorithm, the two classes of algorithms at the forefront of artificial systems inspired by human body mechanism. We then move on to compare these two classes of algorithms on the parameters of population, generation and clone sizes. This comparison will help in the analysis of feasibility of these algorithms for specific purposes. The immune system is a complex of cells, molecules and organs which has proven to be capable of performing several tasks, like pattern recognition, learning, memory acquisition, generation of diversity, noise tolerance, generalization, distributed detection and optimization. Based on immunological principles, new computational techniques are being developed, aiming not only at a better understanding of the system, but also at solving engineering problems. We discuss the main strategy used by the immune system to problem solving, and introduce the concept of immune engineering. The immune engineering makes use of immunological concepts in order to create tools for solving demanding machine-learning problems using information extracted from the problems themselves. A genetic algorithm (GA) is a search heuristic that mimics the process of natural evolution. This heuristic is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover. A genetic algorithm (GA) is a search heuristic that mimics the process of natural evolution. This heuristic is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover.