Genetic algorithms and Darwinian approaches in financial applications: A survey

•A survey of evolutionary computation applied to finance.•GAs, GP, LCS, MOEAS, EDAS and co-evolution approaches are covered.•This new version makes a revision of similar surveys to refine the scope.•An analysis of past and new references allowed determines changes of interest in the field.•The unexplored combinations of problems and solution methods are indicated and discussed. This article presents a review of the application of evolutionary computation methods to solving financial problems. Genetic algorithms, genetic programming, multi-objective evolutionary algorithms, learning classifier systems, co-evolutionary approaches, and estimation of distribution algorithms are the techniques considered. The novelty of our approach comes in three different manners: it covers time lapses not included in other review articles, it covers problems not considered by others, and the scope covered by past and new references is compared and analyzed. The results concluded the interest about methods and problems has changed through time. Although, genetic algorithms have remained the most popular approach in the literature. There are combinations of problems and solutions methods which are yet to be investigated.