Summary and Discussion
Bernabè Dorronsoro, Patricia Ruiz, Grégoire Danoy, Yoann Pigné, Pascal Bouvry · 2014
This chapter pays special attention to evolutionary algorithms (EAs), which is a broad family of well-known population-based optimization algorithms. In EAs, the population of solutions evolves thanks to the use of genetic operators, which combine the information of different solutions to generate new ones, and the survival of the fittest solutions. The chapter briefly summarizes the behavior of the different algorithms on the four studied problems. It gives the reader some hints to choose an appropriate optimization algorithm, by discussing the performance of the different algorithms studied here for all problems. The chapter presents some additional discussion on the performance of the algorithms for the single- and multi-objective problems, respectively. It comments on the convergence speed of the algorithms for the singleobjective problems. This chapter gives some hints in order to help the reader on the choice of the algorithm to use when solving similar problems. Controlled Vocabulary Terms evolutionary computation; optimisation