A meta-GA (GA within a GA) is used to investigate evolving the parameter settings of genetic operators for genetic and evolutionary algorithms (GEA) in the hope of creating a self...
Jeff Clune, Sherri Goings, Bill Punch, Eric Goodma...
In the design of evolutionary multiobjective optimization (EMO) algorithms, it is important to strike a balance between diversity and convergence. Traditional mask-based crossover...
Mutation-based Evolutionary Algorithms, also known as Evolutionary Programming (EP) are commonly applied to Artificial Neural Networks (ANN) parameters optimization. This paper pre...
Kristina Davoian, Alexander Reichel, Wolfram-Manfr...
The effective reverse engineering of biochemical networks is one of the great challenges of systems biology. The contribution of this paper is two-fold: 1) We introduce a new meth...
Gene Expression Programming (GEP) is an evolutionary algorithm that incorporates both the idea of a simple, linear chromosome of fixed length used in Genetic Algorithms (GAs) and...
Qiongyun Zhang, Chi Zhou, Weimin Xiao, Peter C. Ne...