Evolutionary Approach to Non-stationary Optimisation Tasks

Most real-world applications operate in dynamic environments. In such environments often it is necessary to modify the current solution due to various changes in the environment (e.g., machine breakdowns, sickness of employees, etc). Thus it is important to investigate properties of adaptive algorithms which do not require re-start every time a change is recorded. In this paper non-stationary problems (i.e., problems, which change in time) are discussed. We describe different types of changes in the environment. A new model for non-stationary problems and a classification of these problems by the type of changes is proposed. A brief review of existing applied measures of obtained results is also presented.

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

[2]  David E. Goldberg,et al.  Nonstationary Function Optimization Using Genetic Algorithms with Dominance and Diploidy , 1987, ICGA.

[3]  Zbigniew Michalewicz,et al.  Parameter control in evolutionary algorithms , 1999, IEEE Trans. Evol. Comput..

[4]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .

[5]  S. Tsutsui,et al.  Function optimization in nonstationary environment using steady state genetic algorithms with aging of individuals , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[6]  Michèle Sebag,et al.  Inductive Learning of Mutation Step-Size in Evolutionary Parameter Optimization , 1997, Evolutionary Programming.

[7]  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).

[8]  Dipankar Dasgupta,et al.  Nonstationary Function Optimization using the Structured Genetic Algorithm , 1992, PPSN.

[9]  Robert G. Reynolds,et al.  Knowledge-based self-adaptation in evolutionary programming using cultural algorithms , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

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

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

[12]  Thomas Bäck,et al.  Intelligent Mutation Rate Control in Canonical Genetic Algorithms , 1996, ISMIS.

[13]  Terence C. Fogarty,et al.  Learning the local search range for genetic optimisation in nonstationary environments , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[14]  Michèle Sebag,et al.  Toward Civilized Evolution: Developing Inhibitions , 1997, ICGA.

[15]  David E. Goldberg,et al.  Genetic Algorithms with Sharing for Multimodalfunction Optimization , 1987, ICGA.

[16]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.