Abstract- The Pareto optimal solutions to a multiobjective optimization problem often distribute very regularly in both the decision space and the objective space. Most existing ev...
Aimin Zhou, Qingfu Zhang, Yaochu Jin, Edward P. K....
Abstract- Seeding the population of an evolutionary algorithm with solutions from previous runs has proved to be useful when learning control strategies for agents operating in a c...
Mitchell A. Potter, R. Paul Wiegand, H. Joseph Blu...
Real-world applications generally distinguish themselves from theoretical developments in that they are much more complex and varied. As a consequence, better models require more d...
In case the objective function to be minimized is not known analytically and no assumption can be made about the single extremum, global optimization (GO) methods must be used. Pap...
Abstract— This paper presents a Markov model for the convergence of multi-parent genetic algorithms (MPGAs). The proposed model formulates the variation of gene frequency caused ...
An L-system or Lindenmayer system consists of a grammar and an interpreter. The grammar contains an axiom, usually a short string, that the grammar expands into a long, complex st...
Daniel A. Ashlock, Stephen P. Gent, Kenneth Mark B...
We present a homomorphous mapping that converts problems with linear equality constraints into fully unconstrained and lower-dimensional problems for optimization with PSO. This ap...
—In this paper, the performance of dynamic multi-swarm particle swarm optimizer (DMS-PSO) on the set of benchmark functions provided for the CEC2008 Special Session on Large Scal...
The XCS Learning Classifier System has traditionally used roulette wheel selection within its genetic algorithm component. Recently, tournament selection has been suggested as prov...