In this paper we compare Mixed-Integer Evolution Strategies (MI-ES) and standard Evolution Strategies (ES) when applied to find optimal solutions for artificial test problems and ...
Rui Li, Michael Emmerich, Jeroen Eggermont, Ernst ...
Genetic programming has been considered a promising approach for function approximation since it is possible to optimize both the functional form and the coefficients. However, it...
In this paper, an object-oriented unified optimization framework (UOF) for general problem optimization is proposed. Based on evolutionary algorithms, numerical deterministic meth...
This paper describes an extension to a speciation-based particle swarm optimizer (SPSO) to improve performance in dynamic environments. The improved SPSO has adopted several prove...
Wire routing in a VLSI chip often requires minimization of wire-length as well as the number of intersections among multiple nets. Such an optimization problem is computationally ...
Rajeev Kumar, Pramod Kumar Singh, Bhargab B. Bhatt...
We propose a hybrid algorithm (called ALPINE) between Genetic Algorithm and Dantzig's Simplex method to approximate optimal solutions for the Flexible Job-Shop Problem. Local...
The conversion and extension of the Incremental ParetoCoevolution Archive algorithm (IPCA) into the domain of Genetic Programming classifier evolution is presented. In order to ac...
Action set selection in Markov Decision Processes (MDPs) is an area of research that has received little attention. On the other hand, the set of actions available to an MDP agent...
Genetic Parallel Programming (GPP) is a novel Genetic Programming paradigm. Based on the GPP paradigm and a local search operator - FlowMap, a logic circuit synthesizing system in...