There are so many objects or processes going on in nature, without us being aware of it. Computers scientists have designed algorithms to process and analyze this biological data, and likewise, biologists have discovered several operating principles that have inspired new optimization methods. Thinking computationally about biological processes can be used to improve the design of algorithms. Computer scientists have also relied on biological systems for inspiration, especially when developing optimization methods. I have visualized a few of these algorithms and optimization methods in the following illustrations; a genetic algorithm, a particle swarm optimization and the golden spiral / Koch snowflake. The genetic algorithm is inspired by population genetics (including heredity and gene frequencies), and evolution at the population level, as well as the Mendelian understanding of the structure (such as chromosomes, genes, alleles) and mechanisms (such as recombination and mutation). The strategy for the Genetic Algorithm is to repeatedly employ surrogates for the recombination and mutation genetic mechanisms on the population of candidate solutions. The particle swarm optimization is inspired by the social foraging behavior of some animals such as flocking behavior of birds and the schooling behavior of fish. The goal of the algorithm is to have all the particles locate the optima in a multi-dimensional hyper-volume. This is achieved by assigning initially random positions to all particles in the space and small initial random velocities. The golden spiral is a logarithmic spiral whose growth factor is φ, the golden ratio. Koch snow- flake is A fractal, also known as the Koch island, which was first described by Helge von Koch in 1904. It is built by starting with an equilateral trian- gle, removing the inner third of each side, building another equilateral triangle at the location where the side was removed, and then repeating the process indefinitely.