AbstractThe purpose of this paper is to describe and evaluate a new algorithm for optimization. The new algorithm is named the Genetic Flock Algorithm. This algorithm is a type of hybrid of a Genetic Algorithm and a Particle Swarm Optimization Algorithm. The paper discusses strengths and weaknesses of these two algorithms. It then explains how the Genetic Flock Algorithm combines features of both and gives details of the algorithm. All three algorithms are compared using eight standard optimization problems that are used in the literature. It is shown that the Genetic Flock Algorithm provides superior performance on 75% of the tested cases. In the remaining 25% of the cases it outperforms either the Genetic Algorithm or the Particle Swarm Optimization Algorithm; it is never worse than both. Possible future improvements to the Genetic Flock Algorithm are briefly described.