Evola: An Evolutionary-Algorithm Software for Optimization of Complex Genetic Problems

Evolutionary algorithms represent a robust alternative to optimize complex non-linear problems when the number of possible combination of solutions is prohibitive to be computed. Based in quantitative genetics and evolutionary theory these type of algorithms represent a flexible tool that is little used in many fields due to the little promotion of available software or due to a complex specification required. Open-source evolutionary algorithms exist in different languages, but here we present an intuitive machinery for biologists and non-biologists that follows the quantitative genetics and evolutionary theory named evola. The evola R package allows the user to specify problems in a formula-based (traits/features to optimize and the genes/QTLs behind the traits) as a breeding population. Two optimization examples (selection of optimal /individuals in breeding and general constrained optimization) showing the flexibility of the evola software are presented. Customized fitness functions and constraints can be provided easily, and results are in the standard AlphaSimR format that can be later investigated as populations of solutions.