Two adaptive mutation operators for optima tracking in dynamic optimization problems with evolution strategies

The dynamic optimization problem concerns finding an optimum in a changing environment. In the tracking problem, the optimizer should be able to follow the optimum's changes over time.In this paper we present two adaptive mutation operators designed to improve the following of a time-changing optimum, under the assumption that the changes follow a non-random law. Such law can be estimated in order to improve the optimum tracking capabilities of the algorithm. For experimental assessment, a (1,λ) evolution strategy has been applied to a dynamic version of the sphere problem.

[1]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[2]  Nikolaus Hansen,et al.  Step-Size Adaption Based on Non-Local Use of Selection Information , 1994, PPSN.

[3]  T. Back,et al.  On the behavior of evolutionary algorithms in dynamic environments , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[4]  Peter A. N. Bosman,et al.  Learning, anticipation and time-deception in evolutionary online dynamic optimization , 2005, GECCO '05.

[5]  John J. Grefenstette,et al.  Case-Based Initialization of Genetic Algorithms , 1993, ICGA.

[6]  John J. Grefenstette,et al.  Genetic Algorithms for Changing Environments , 1992, PPSN.

[7]  N. Hansen,et al.  Step-Size Adaptation Based on Non-Local Use Selection Information , 1994 .

[8]  Phillip D. Stroud,et al.  Kalman-extended genetic algorithm for search in nonstationary environments with noisy fitness evaluations , 2001, IEEE Trans. Evol. Comput..

[9]  Hans-Georg Beyer,et al.  Optimum Tracking with Evolution Strategies , 2006, Evolutionary Computation.

[10]  Hajime Kita,et al.  Adaptation to Changing Environments by Means of the Memory Based Thermodynamical Genetic Algorithm , 1997, ICGA.

[11]  Peter J. Angeline,et al.  Tracking Extrema in Dynamic Environments , 1997, Evolutionary Programming.

[12]  Jürgen Branke,et al.  Evolutionary optimization in uncertain environments-a survey , 2005, IEEE Transactions on Evolutionary Computation.

[13]  Hans-Georg Beyer,et al.  The Theory of Evolution Strategies , 2001, Natural Computing Series.

[14]  Hussein A. Abbass,et al.  Oiling the Wheels of Change: The Role of Adaptive Automatic Problem Decomposition in Non-Stationary Environments , 2005, ArXiv.

[15]  Terence C. Fogarty,et al.  A Genetic Algorithm with Variable Range of Local Search for Tracking Changing Environments , 1996, PPSN.

[16]  Jürgen Branke,et al.  Memory enhanced evolutionary algorithms for changing optimization problems , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[17]  W. Cedeno,et al.  On the use of niching for dynamic landscapes , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[18]  Jürgen Branke,et al.  A Multi-population Approach to Dynamic Optimization Problems , 2000 .

[19]  Jrgen Branke,et al.  Evolutionary approaches to dynamic optimization problems , 2001 .

[20]  Helen G. Cobb,et al.  An Investigation into the Use of Hypermutation as an Adaptive Operator in Genetic Algorithms Having Continuous, Time-Dependent Nonstationary Environments , 1990 .

[21]  K. Weicker,et al.  On evolution strategy optimization in dynamic environments , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).