Production scheduling dictates highly constrained mathematical models with complex and often contradicting objectives. Evolutionary algorithms can be formulated almost independently of the detailed shaping of the problems under consideration. As one would expect, a weak formulation of the...

After a decade of development, genetic algorithms and genetic programming have become a widely accepted toolkit for computational finance. Genetic Algorithms and Genetic Programming in Computational Finance is a pioneering volume devoted entirely to a systematic and comprehensive review of this...

Multiobjective Scheduling by Genetic Algorithms describes methods for developing multiobjective solutions to common production scheduling equations modeling in the literature as flowshops, job shops and open shops. The methodology is metaheuristic, one inspired by how nature has evolved a...

The procurement of transportation services via large-scale combinatorial auctions involves a couple of complex decisions whose outcome highly influences the performance of the tender process. This paper examines the shipper's task of selecting a subset of the submitted bids which efficiently...

Human population is chaotic (complex) and the people in both systems are equally complex (dynamical system). Thus, the theory of dynamical system best explains the growth of human population. Symbolic regression (SR) with genetic programming (GP) is a model which uses the ideas of biological...