Variable-Size Memory Evolutionary Algorithm to Deal with Dynamic Environments

When dealing with dynamic environments two major aspects must be considered in order to improve the algorithms' adaptability to changes: diversity and memory. In this paper we propose and study a new evolutionary algorithm that combines two populations, one playing the role of memory, with a biological inspired recombination operator to promote and maintain diversity. The size of the memory mechanism may vary along time. The size of the (usual) search population may also change in such a way that the sum of the individuals in the two populations does not exceed an established limit. The two populations have minimum and maximum sizes allowed that change according to the stage of the evolutionary process: if an alteration is detected in the environment, the search population increases its size in order to readapt quickly to the new conditions. When it is time to update memory, its size is increased if necessary. A genetic operator, inspired by the biological process of conjugation, is proposed and combined with this memory scheme. Our ideas were tested under different dynamics and compared with other approaches on two benchmark problems. The obtained results show the efficacy, efficiency and robustness of the investigated algorithm.

[1]  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).

[2]  Shengxiang Yang,et al.  Memory-based immigrants for genetic algorithms in dynamic environments , 2005, GECCO '05.

[3]  Xin Yao,et al.  Experimental study on population-based incremental learning algorithms for dynamic optimization problems , 2005, Soft Comput..

[4]  David W. Pearson,et al.  An Immune System-Based Genetic Algorithm to Deal with Dynamic Environments: Diversity and Memory , 2003, ICANNGA.

[5]  C. Ryan,et al.  The Degree of Oneness , 2007 .

[6]  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 .

[7]  A. Sima Etaner-Uyar,et al.  A new population based adaptive domination change mechanism for diploid genetic algorithms in dynamic environments , 2005, Soft Comput..

[8]  Jürgen Branke,et al.  Evolutionary Optimization in Dynamic Environments , 2001, Genetic Algorithms and Evolutionary Computation.

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

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

[11]  Mark Wineberg,et al.  Enhancing the GA's Ability to Cope with Dynamic Environments , 2000, GECCO.

[12]  K. De Jong,et al.  The usefulness of tag bits in changing environments , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[13]  Shengxiang Yang,et al.  Non-stationary problem optimization using the primal-dual genetic algorithm , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[14]  Shengxiang Yang,et al.  A comparative study of immune system based genetic algorithms in dynamic environments , 2006, GECCO.

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

[16]  Shengxiang Yang,et al.  Associative Memory Scheme for Genetic Algorithms in Dynamic Environments , 2006, EvoWorkshops.

[17]  Larry Wos,et al.  What Is Automated Reasoning? , 1987, J. Autom. Reason..

[18]  Christoph F. Eick,et al.  Supporting Polyploidy in Genetic Algorithms Using Dominance Vectors , 1997, Evolutionary Programming.

[19]  S. Louis,et al.  Genetic Algorithms for Open Shop Scheduling and Re-scheduling , 1996 .

[20]  Zbigniew Michalewicz,et al.  Searching for optima in non-stationary environments , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

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

[22]  Anabela Simões,et al.  A Comparative Study Using Genetic Algorithms to Deal with Dynamic Environments , 2003, ICANNGA.

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

[24]  Anabela Simões,et al.  Transposition: A Biological-Inspired Mechanism to Use with Genetic Algorithms , 1999, ICANNGA.

[25]  R.W. Morrison,et al.  Triggered hypermutation revisited , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[26]  Inman Harvey,et al.  The Microbial Genetic Algorithm , 2009, ECAL.

[27]  B. Bainbridge,et al.  Genetics , 1981, Experientia.

[28]  Peter Ross,et al.  Useful diversity via multiploidy , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[29]  Anabela Simões,et al.  An Immune System-Based Genetic Algorithm to Deal with Dynamic Environments: Diversity and Memory , 2003, ICANNGA.

[30]  Kok Cheong Wong,et al.  A New Diploid Scheme and Dominance Change Mechanism for Non-Stationary Function Optimization , 1995, ICGA.