An Evolutionary Algorithm is a subset of evolutionary computation in artificial intelligence. Evolutionary computation is a family of algorithms for global optimization inspired by biological evolution. The increasing complexity of the real-world optimization problems due to the rapid development of the information age and "Big Data", puts forth new challenges to evolutionary computation. Distributed evolutionary computation with distributed evolutionary algorithm is a solution to respond to these challenges. Distributed Evolutionary Algorithms follow a divide-and-conquer mechanism and offer an opportunity to solve complex high dimensional optimization problems. They can be implemented on parallel as well as distributed systems. The induction of parallelism in a distributed architecture for evolutionary computation increases its cost-effectiveness and performance by decreasing the time to computation. Additionally, in the context of parallel evolutionary algorithms, asynchronous evolutionary algorithms help to eliminate the idle CPU time induced by synchronous evolutionary algorithms and offer an efficient use of parallelization. However, asynchronous evolutionary algorithms can also induce an evaluation-time bias towards fast evaluating individuals in the search space. This structured presentation provides valuable insights on the different models of distributed evolutionary algorithms and also highlights upon asynchronous parallel evolutionary algorithms & their problem of evaluation-time bias with a description of the research attempted towards the elimination of evaluation-time bias.