Package ec.multiobjective.spea2

Strength Pareto Evolutionary Algorithm implementation.

See:
          Description

Class Summary
SPEA2Breeder Breeds each subpopulation separately, with no inter-population exchange, and using the SPEA2 approach.
SPEA2Evaluator The SPEA2Evaluator is a simple, non-coevolved generational evaluator which evaluates every single member of every subpopulation individually in its own problem space.
SPEA2MultiObjectiveFitness SPEA2MultiObjectiveFitness is a subclass of Fitness which implements basic multiobjective fitness functions along with support for the ECJ SPEA2 (Strength Pareto Evolutionary Algorithm) extensions.
SPEA2Subpopulation SPEA2Subpopulation is a simple subclass of Subpopulation which adds the archiveSize field.
SPEA2TournamentSelection Does a simple tournament selection, limited to the subpopulation it's working in at the time and only within the boundry of the SPEA2 archive (between 0-archiveSize).
 

Package ec.multiobjective.spea2 Description

Strength Pareto Evolutionary Algorithm implementation.

Details of this approach can be found in the following paper: E. Zitzler, M. Laumanns, and L. Thiele.SPEA2: Improving the Performance of the Strength Pareto Evolutionary Algorithm. Technical Report 103, Computer Engineering and Communication Networks Lab (TIK), Swiss Federal Institute of Technology (ETH) Zurich, Gloriastrasse 35, CH-8092 Zurich, May 2001. (Postscript), (PDF).

The Strength Pareto Evolutionary Algorithm (SPEA) (Zitzler and Thiele 1999) is a relatively recent technique for finding or approximating the Pareto-optimal set for multiobjective optimization problems. In different studies (Zitzler and Thiele 1999; Zitzler, Deb, and Thiele 2000) SPEA has shown very good performance in comparison to other multiobjective evolutionary algorithms, and therefore it has been a point of reference in various recent investigations, e.g., (Corne, Knowles, and Oates 2000). Furthermore, it has been used in different applications, e.g., (La-hanas, Milickovic, Baltas, and Zamboglou 2001). In this paper, an improved ver-sion, namely SPEA2, is proposed, which incorporates in contrast to its predecessor a fine-grained fitness assignment strategy, a density estimation technique, and an enhanced archive truncation method. The comparison of SPEA2 with SPEA and two other modern elitist methods, PESA and NSGA-II, on different test problems yields promising results.

Author:
Robert Hubley, Insitute for Systems Biology. With modifications by Sean Luke.