A Review for the Genetic Algorithm and the Red Deer Algorithm Applications

The Red Deer algorithm (RD), a contemporary population-based meta heuristic algorithm, applications are thoroughly examined in this paper. The RD algorithm blends evolutionary algorithms' survival of the fittest premise with the productivity and richness of heuristic search approaches. On the other a well-known and relatively older evolutionary based algorithm called the Genetic Algorithm applications are also shown. The contemporary algorithm; the RDA, and the older algorithm; the GA have wide applications in computer science and engineering. This paper sheds the light on all those applications and enable researchers to exploit the possibilities of adapting them in any applications they may have either in engineering, computer science, or business.

1. Introduction

The Red Deer Algorithm (RDA) is a modern meta-heuristic search method that has evolutionary-based traits like survivability for the fittest, reproduction, the potential to create new solutions, and convergence toward optimal answers. As a herd of solutions usually follows the leader and supports its hunt for better solutions, it acts as a Swarm Intelligence algorithm. The RDA has been employed in a variety of scientific, business, and engineering applications. First proposed the RDA in 2016 [1]–[3], It was described as a novel algorithm based on Red Deer mating, please see Figure. 1 below. They used this pairing to create an optimization algorithm, which they then tested against a benchmark of multi-variable, high-complexity mathematical optimization problems. The results were promising, outperforming a number of well-known algorithms. The same authors utilized the method to improve traditional computer science algorithms like the Traveling Salesman Problem (TSP) and found that it outperformed dominant algorithms like the Genetic Algorithm. There are adaptive and modified variants of this algorithm just to help tackle difficult optimization issues, such as [4]–[6], and many others, which we will describe in the next parts. On the other hand, the GA is a well-established algorithm that has been around lonf time ago and its efficiency and capabilities were demonstrated in many applications that we will mention in the coming sections [7].