Some of the latest work done in the field of Evolution Algorithms are discussed. Evolutionary Algorithms (EA) using the same principles as of natural evolution can be used as powerful search and optimization methods. EAs are generic and robust optimization methods since they do not make any assumption about the underlying search space. The paper by Hu et al. proposes a novel Ant Colony Optimization (ACO) algorithm, Continuous Orthogonal Ant Colony (COAC), designed specifically for handling continuous optimization problems as opposed to the conventional ACO algorithms dealing with only discrete problems. The proposed algorithm incorporates the orthogonal design method, which is widely used as an experimental design method in scientific research. The paper by Ding et al. investigates the potential of employing a histogram probabilistic model in the continuous estimation of distribution algorithms (EDA).