A Hybrid Multi-Objective Evolutionary Algorithm Using an Inverse Neural Network

We present two methods to accelerate the search of a Multi-Objective Evolutionary Algorithm (MOEA) using Arti£cial Neural Networks (ANN) to approximate the £tness functions. This approach can substantially reduce the number of £tness evaluations on computational expensive problems without compromising the good search capabilities of MOEA. In one method the ANN is used to approximate the global £tness functions and in the second the ANN is applied in a local search strategy to discover better individuals from previous generations. The ef£ciency of both methods is tested on several benchmark functions.

[1]  R. Weiner.  Lecture Notes in Economics and Mathematical Systems , 1985 .

[2]  Joshua D. Knowles,et al.  On metrics for comparing nondominated sets , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[3]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[4]  Bernhard Sendhoff,et al.  A framework for evolutionary optimization with approximate fitness functions , 2002, IEEE Trans. Evol. Comput..

[5]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[6]  Antonio Cunha,et al.  Modelling and Optimisation of Single Screw Extrusion , 1999 .

[7]  Larry Bull,et al.  On Model-Based Evolutionary Computation , 1999, Soft Comput..

[8]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[9]  C. Poloni,et al.  Hybridization of a multi-objective genetic algorithm, a neural network and a classical optimizer for a complex design problem in fluid dynamics , 2000 .

[10]  David W. Corne,et al.  Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy , 2000, Evolutionary Computation.

[11]  J. D. Schaffer,et al.  Some experiments in machine learning using vector evaluated genetic algorithms (artificial intelligence, optimization, adaptation, pattern recognition) , 1984 .

[12]  D. Corne,et al.  On Metrics for Comparing Non Dominated Sets , 2001 .

[13]  António Gaspar-Cunha,et al.  Use of Genetic Algorithms in Multicriteria Optimization to Solve Industrial Problems , 1997, ICGA.

[14]  Peter J. Fleming,et al.  Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization , 1993, ICGA.

[15]  Kalyanmoy Deb,et al.  A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II , 2000, PPSN.

[16]  Eckart Zitzler,et al.  Evolutionary algorithms for multiobjective optimization: methods and applications , 1999 .