Real-Parameter Optimization by Iterative Prototype Optimization with Evolved Improvement Steps

Evolutionary algorithms are typically used to evolve a population of complete candidate solutions to a given problem. Recently, a novel framework called iterative prototype optimization with evolved improvement steps has been proposed. This is a general optimization framework, where a possible improvement of a prototype solution is being evolved by the evolutionary algorithm. The framework has already been used to solve binary string optimization problems and the combinatorial optimization problem. In this paper we use this optimization framework to solve real-parameter optimization problems. The algorithm was tested on problems collected for the Special Session on real-parameter optimization of the IEEE Congress on Evolutionary Computation 2005. The achieved results show a potential of the presented optimization framework for solving hard real-parameter optimization problems.