Inheriting Parents Operators: A New Dynamic Strategy for Improving Evolutionary Algorithms

On-the-fly calibrating strategies for evolutionary algorithms☆

“…These strategies, include information in the individuals that influence the parameter values and, define a set of criteria to produce changes. In [30], the authors propose a method that allows each individual to select the crossover operator according to the information inherited from its parents. Gómez introduced the Hybrid Adaptive Evolutionary Algorithm (HAEA) in [12], a self-adaptive dynamic parameter control for genetic operators.…”

Eda: An Evolutionary Decoding Algorithm for Statistical Machine Translation

“…The goal is to find the best combination of the parameters changing their values during the execution, according to the state of the search (Riff and Bonnaire 2002). In the beginning all the operator's probabilities are equal.…”

Calibrating strategies for evolutionary algorithms

“…For that reason, the idea to design strategies to allow the algorithm to change its parameter values during its execution appears a promising way to improve the performance of an evolutionary algorithm. In this context many ideas have been proposed in the research community, amongst them we find self-adaptability processes where the individuals include information that can influence the parameter values and the evolution of these processes could use some criteria to produce changes, [7], [1]. Another interesting method has been proposed in [6].…”