Phenotype evolution as optimization

Biological evolution can be described as a population climbing a fitness landscape, and has inspired a variety of derivative-free optimization algorithms. Here we describe how phenotype evolution has sophisticated optimization properties. In particular, natural selection approximates second order gradient descent (Newton's method), and recombination is efficient in generating diversity. We use these insights to design a new type of derivative-free optimization algorithm for continuous problems.Non UBCUnreviewedAuthor affiliation: MPI for Dynamics and Self-organizationPostdoctora