A genetic algorithm with multiple populations (MPGA) is described. Two operators of the algorithm, local selection and the multiple population concept, are discussed in detail.
A new method for fitness assignment by ranking using a non-linear distribution is presented. The use of non-linear ranking permits higher selective pressures than the conventional ranking methods.
The algorithm was implemented in Matlab and is available as part of the Genetic Algorithm Toolbox for Matlab .
Different main schools of evolutionary algorithms evolved during the last 30 years: genetic algorithms, mainly developed in the USA by J. H. Holland , evolutionary strategies, developed in Germany by I. Rechenberg  and H.-P. Schwefel  and evolutionary programming . Each of these constitutes a different approach, however, they are inspired in the same principles of natural evolution. A good introductory survey can be found in .
This paper describes the Multipopulation genetic algorithm. In Section 2 a short overview on the structure and basic algorithms of the genetic algorithm is given. Section 3 describes non-linear ranking. In Section 4 the concept of local selection is presented. Section 4 explains the multiple population concept in detail.