Evolutionary computation is a widely used optimization technique. These algorithms have been applied to numerous problems such as mathematical complex problems and real world problems. The good point about these algorithms is that they give benefits to people who are doing research on very challenging problems that need optimization. Some of the benefits that researchers get are that they are simple to implement and provide a good response to any type of input when the environment is changing at a rapid pace. In real world there are large combinatorial problems that are to be optimized and cannot be solved with exact answers. When these evolutionary algorithms are combined with local search, they provide improved approximation using local search technique. The bad thing about local search used along is that it gets stuck in local optimum, and in this situation the objective function of the problem can be enhanced when the changes are made out of the current state neighbors during the search. This problem gives a way to hybridize the evolutionary algorithms with some other search and optimization methods and also the local search algorithms. Current research paper identifies different ways that can be used for efficiently hybridizing the evolutionary algorithms with other search and optimization techniques to provide optimized solutions to engineering problems especially, the Wireless Sensor Networks (WSN).