IJCSNS International Journal of Computer Science and Network Security, VOL.8 No.3, March 2008 179 Manuscript received March 5, 2008 Manuscript revised March 20, 2008 De Jong’s Sphere Model Test for A Social-Based Genetic Algorithm (SBGA) Nagham Azmi AL-Madi , Ahamad Tajudin Khader School of Computer Sciences, Universiti Sains Malaysia (USM), Penang, Malaysia Summary In this paper, we used the De Jong’s first function 1, “The Sphere Model” to compare values and results concerning the averages and best fits of both, the Simple Standard Genetic Algorithm (SGA), and a new approach of Genetic Algorithms named Social-Based Genetic Algorithm (SBGA). Results from the Sphere Model test on Social-Based Genetic Algorithms (SBGA) are obtained. These results are encouraging in that the Social-Based Genetic Algorithms (SBGA) performs better in finding best fit solutions of generations in different populations than the Simple Standard Genetic Algorithm. Key words: Genetic Algorithms (GAs), ,Evolutionary Algorithms (EA), Simple Standard Genetic Algorithms (SGA), Social-Based Genetic Algorithm (SBGA), De Jongs’ functions, the Sphere model. 1. Introduction In the early 1960s and 1970s, new search algorithms were initially proposed by Holland, his colleagues and his students at the University of Michigan. These search algorithms which are based on nature and mimic the mechanism of natural selection were known as Genetic Algorithms (GAs) [1, 3, 5, 6, 7, 8, 9]. Holland in his book “Adaptation in Natural and Artificial Systems” [1] initiated this area of study. Theoretical foundations besides exploring applications were also presented. As a matter of fact, “Genetic algorithms’ functionality is based upon Darwin's theory of evolution through natural and sexual selection.” [8]. They mimic biological organisms [5]. In GAs a solution to the problem is represented as a genome (or chromosome) [1, 3, 4, 5, 6]. The population of solutions is initialized by applying the GAs operators such as the crossover and mutation [1, 3, 4, 5, 6]. And with their natural selection they have an iterative procedure usually used to optimize and select the best chromosome (solution) in the population. This population consists of various solutions to hard complex problems and is usually generated randomly [5, 14]. Figure (1) below represents the Simple Standard GA evolution flow. Figure (1) Evolution flow of genetic algorithm [5]. GAs attracted many researchers to search and optimize complex problems. In fact, they proved to be efficient in solving different combinatorial optimization problems. They are considered heuristic search algorithms that solve unconstrained and constrained problems [3]. Many applications use these kinds of algorithms in designing complex devices such as aircraft turbines, integrated circuits and many others, GAs play a main role [3].