Global Optimization of Functions with the Interval Genetic Algorithm
Marco Muselli
Istituto per i Circuiti Elettronici,
Consiglio Nazionale delle Ricerche, 16145 Genova, Italy
Sandro Ridella
Dipartimento di Ingegneria Biofisica ed Elettronica,
Universita di Genova, 16145 Genova, Italy
Abstract
A new evolutionary method for the global optimization of functions with continuous variables is proposed. This algorithm can be viewed as an efficient parallelization of the simulated annealing technique, although a suitable interval coding shows a close analogy between real-coded genetic algorithms and the proposed method, called interval genetic algorithm.
Some well-defined genetic operators allow a considerable improvement in reliability and efficiency with respect to conventional simulated annealing even on a sequential computer. Results of simulations on Rosenbrock valleys and cost functions with flat areas or fine-grained local minima are reported.
Furthermore, tests on classical problems in the field of neural networks are presented. They show a possible practical application of the interval genetic algorithm.
