Prospective Bio-Inspired Algorithm-Based Self-organization Approaches for Genetic Algorithms

The genetic algorithm (GA) is a population-based meta-heuristic global optimization technique for dealing with complex problems with very large search space. Nature plays a grand and enormous starting place of inspiration for solving NP completeness problems in biology, mathematics, and computer science. In the interim, it is the use of computers to represent the living phenomena, and concurrently the study of life to progress the usage of computers. Biologically inspired computing is a most important subset of computation. It constantly finds the optimal solution to explain its problem maintaining faultless steadiness among its components. Bio-inspired algorithms are meta-heuristics that imitate the nature for solving optimization problems in computation. Self-organization is the mechanism to improve the fitness and diversity among the population in the optimization algorithms. This paper presents a broad overview to examine several types of bio-inspired algorithm that can be used as a self-organization technique in genetic algorithms to improve its overall performance.