Population-Based Incremental Learning With Associative Memory for Dynamic Environments

In recent years, interest in studying evolutionary algorithms (EAs) for dynamic optimization problems (DOPs) has grown due to its importance in real-world applications. Several approaches, such as the memory and multiple population schemes, have been developed for EAs to address dynamic problems. This paper investigates the application of the memory scheme for population-based incremental learning (PBIL) algorithms, a class of EAs, for DOPs. A PBIL-specific associative memory scheme, which stores best solutions as well as corresponding environmental information in the memory, is investigated to improve its adaptability in dynamic environments. In this paper, the interactions between the memory scheme and random immigrants, multipopulation, and restart schemes for PBILs in dynamic environments are investigated. In order to better test the performance of memory schemes for PBILs and other EAs in dynamic environments, this paper also proposes a dynamic environment generator that can systematically generate dynamic environments of different difficulty with respect to memory schemes. Using this generator, a series of dynamic environments are generated and experiments are carried out to compare the performance of investigated algorithms. The experimental results show that the proposed memory scheme is efficient for PBILs in dynamic environments and also indicate that different interactions exist between the memory scheme and random immigrants, multipopulation schemes for PBILs in different dynamic environments.

[1]  Xin Yao,et al.  Materialized view selection as constrained evolutionary optimization , 2003, IEEE Trans. Syst. Man Cybern. Part C.

[2]  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 .

[3]  K. Atashkari,et al.  Thermodynamic Pareto optimization of turbojet engines using multi-objective genetic algorithms , 2005 .

[4]  Shengxiang Yang,et al.  Evolutionary Computation in Dynamic and Uncertain Environments , 2007, Studies in Computational Intelligence.

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

[6]  Ernesto Costa,et al.  On biologically inspired genetic operators: transformation in the standard genetic algorithm , 2001 .

[7]  John J. Grefenstette,et al.  Genetic Algorithms for Tracking Changing Environments , 1993, ICGA.

[8]  H. Handa,et al.  Robust route optimization for gritting/salting trucks: a CERCIA experience , 2006, IEEE Computational Intelligence Magazine.

[9]  J. A. Lozano,et al.  Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation , 2001 .

[10]  Shengxiang Yang,et al.  Population-based incremental learning with memory scheme for changing environments , 2005, GECCO '05.

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

[12]  X. Yaoc,et al.  A novel evolutionary algorithm for determining uni " ed creep damage constitutive equations , 2002 .

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

[14]  S. Baluja.  An Empirical Comparison of Seven Iterative and Evolutionary Function Optimization Heuristics , 1995 .

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

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

[17]  Shumeet Baluja,et al.  A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning , 1994 .

[18]  B. Li,et al.  A novel evolutionary algorithmfor determ ining uni"ed creep damage constitutive equations , 2002 .

[19]  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).

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

[21]  Shengxiang Yang,et al.  A comparative study of immune system based genetic algorithms in dynamic environments , 2006, GECCO '06.

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

[23]  Elizabeth W. Jones,et al.  Genetics:Principles and Analysis 4th ed , 1998 .

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

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

[26]  Zbigniew Michalewicz,et al.  Evolutionary optimization in non-stationary environments , 2000 .

[27]  David E. Goldberg,et al.  The Design of Innovation: Lessons from and for Competent Genetic Algorithms , 2002 .

[28]  Emma Hart,et al.  A Comparison of Dominance Mechanisms and Simple Mutation on Non-stationary Problems , 1998, PPSN.

[29]  Daniel L. Hartl,et al.  Genetics: Principles and Analysis , 1997 .

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

[31]  David W. Pearson,et al.  Improving the Genetic Algorithm ’ s Performance when Using Transformation , 2004 .

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

[33]  John J. Grefenstette,et al.  Evolvability in dynamic fitness landscapes: a genetic algorithm approach , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[34]  Philippe Collard,et al.  Two models of immunization for time dependent optimization , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[35]  Rich Caruana,et al.  Removing the Genetics from the Standard Genetic Algorithm , 1995, ICML.

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

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

[38]  Xin Yao,et al.  Robust Solution of Salting Route Optimisation Using Evolutionary Algorithms , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[39]  David W. Pearson,et al.  An Immune System-Based Genetic Algorithm to Deal with Dynamic Environments : Diversity and Memory , 2004 .

[40]  Ronald W. Morrison,et al.  Designing Evolutionary Algorithms for Dynamic Environments , 2004, Natural Computing Series.

[41]  Pedro Larrañaga,et al.  Estimation of Distribution Algorithms , 2002, Genetic Algorithms and Evolutionary Computation.

[42]  H. Mühlenbein,et al.  From Recombination of Genes to the Estimation of Distributions I. Binary Parameters , 1996, PPSN.

[43]  R.W. Morrison,et al.  A test problem generator for non-stationary environments , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

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

[45]  T. Krink,et al.  Dynamic memory model for non-stationary optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[46]  L. Darrell Whitley,et al.  Fundamental Principles of Deception in Genetic Search , 1990, FOGA.

[47]  Philippe Collard,et al.  From GAs to artificial immune systems: improving adaptation in time dependent optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

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

[49]  Shengxiang Yang,et al.  A self-organizing random immigrants genetic algorithm for dynamic optimization problems , 2007, Genetic Programming and Evolvable Machines.

[50]  Xin Yao,et al.  Thermodynamic Pareto optimization of turbojet engines using multi-objective genetic algorithms , 2005 .

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

[52]  Shengxiang Yang,et al.  Genetic Algorithms with Memory- and Elitism-Based Immigrants in Dynamic Environments , 2008, Evolutionary Computation.

[53]  Shengxiang Yang,et al.  Constructing dynamic test environments for genetic algorithms based on problem difficulty , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).