Aitsaadi et al. [13] derived a new WSN deployment strategy named multi-objective deployment algorithm based on evolutionary and neighborhood search algorithms.
Typically this approach first performs a global search based on evolutionary strategy followed by a local search.
This work presents a novel search algorithm based on evolutionary computation to approximate the optimum array configuration.
We chose commercial software that is similar in nature to the proposed procedure, namely, it treats the objective function as a black box and the search is based on evolutionary optimization techniques.
Based on evolutionary computation concepts, the proposed algorithm searches stochastically along the state space for the best robot's pose estimate.
The search technique is based on evolutionary computing and is designed to be efficient.
Genetic algorithm is an optimization method based on evolutionary biology that is widely used to solve search and optimization problems [ 20- 22].
Genetic algorithms (GAs) are a class of heuristic search algorithms and a particular form of optimization methods based on evolutionary concepts.
Considering that the grid search does not always provide the optimal set of parameters [74], more sophisticated algorithms based on evolutionary programming can be used thus contributing even more accurate models.
An objective function was used throughout the searching process, as well as the same degrees of freedom used by five different meta-heuristic techniques based on evolutionary algorithms.
