ABSTRACT
In dynamic environments, the absence of diversity may degrade the performance of evolutionary algorithms (EAs). In a previous article, we introduced an method, diversity-reference adaptive control (DRAC), to control population diversity based on reference diversity. DRAC aims to track an appropriate diversity level through a control-based strategy. In such a strategy, the evolutionary process is seen as a control problem, in which the process output is the population diversity and the process input is one or more EA adjustable parameters. In that first version of DRAC, the evolutionary process is treated as a black box, thus, the updating of the control variables is made as a function of the error between the population diversity and the reference-model diversity. The DRAC approach does not consider sensitivity analysis. In the current version, a population dynamics model is used to describe the evolutionary process and to allow the control variables updating.
- K. J. Aström and B. Wittenmark. Adaptive Control. Addison-Wesley, 2 edition, 1995.Google Scholar
- J. Branke and H. Schmeck. Designing Evolutionary Algorithms for Dynamic Optimization Problems. Advances in Evolutionary Computing: Theory and Applications, 2003. Google ScholarDigital Library
- H. G. Cobb. An investigation into the use of hypermutation as an adaptive operator in genetic algorithms having continuous, time-dependent nonstationary environments. Technical Report Technical Rep. AIC-90-001, Naval Research Laboratory, Washington, 1990.Google ScholarCross Ref
- J. F. Crow and M. Kimura. An Introduction to Population Genetic Theory. Burgess Publishing, Minnesota, 1970.Google Scholar
- R. A. Fisher. The Genetical Theory of Natural Selection. Oxford University Press, 1930.Google Scholar
- M. M. Gouvêa Jr. and A. F. R. Araújo. Diversity-based model reference for genetic algorithms in dynamic environment. In 2007 IEEE Congress on Evolutionary Computation, CEC'07, Singapore, South Korea, September, 25--28 2007. IEEE Press.Google ScholarCross Ref
- M. M. Gouvêa Jr. and A. F. R. Araújo. Diversity control based on population heterozygosity dynamic. In 2008 IEEE Congress on Evolutionary Computation, CEC'08, Hong Kong, 2008.Google ScholarCross Ref
- M. M. Gouvêa Jr. and A. F. R. Araújo. Population dynamic model for gene frequency prediction in evolutionary algorithms. In 2008 IEEE Congress on Evolutionary Computation, CEC'08, Hong Kong, 2008.Google ScholarCross Ref
- J. J. Grefenstette. Genetic algorithms for changing environments, volume 2, pages 137--144. North-Holland, 1992.Google Scholar
- F. Herrera and M. Lozano. Two-loop real-coded genetic algorithms with adaptive control of mutation step sizes. Applied Intelligence, 13(3):187--204, nov 2000. Google ScholarDigital Library
- Y. Jin and J. Branke. Evolutionary optimization in uncertain environments: a survey. IEEE Transactions on Evolutionary Computation, 9(3):303--317, 2005. Google ScholarDigital Library
- P. Leberg. Genetic approaches for estimating the effective size of populations. Journal of Wildlife Management, 69(4):1385--1399, 2005.Google ScholarCross Ref
- A. E. Magurran. Measuring Biological Diversity. Blackwell, Oxford, UK, 2004.Google Scholar
- R. W. Morrison. Designing Evolutionary Algorithms for Dynamic Environments. Springer, Berlin, 2004. Google ScholarDigital Library
- M. K. Schwartz, D. A. Tallmon, and G. Luikart. Review of DNA-based census and effective population size estimators. Animal Conservation, 1:293--299, 1998.Google ScholarCross Ref
- H.-P. Schwefel. Evolution and Optimum Seeking. John Wiley, Chichester, UK, 1995. Google ScholarDigital Library
- H. Shimodaira. A diversity control-oriented-genetic algorithm (DCGA): performance in function optimization. In 2001 IEEE Congress on Evolutionary Computation, pages 44--51, Seoul, Korea, 2001.Google ScholarCross Ref
- R. E. Smith and D. E. Goldberg. Diploidy and dominance in artificial genetic search. Complex Systems, 6(3):251--285, 1992.Google Scholar
- R. K. Ursem. Diversity-guided evolutionary algorithms. In Lecture Notes in Computer Science, pages 462--474. Springer, 2003. Google ScholarDigital Library
- R. Vencovsky and J. Crossa. Variance Effective Population Size under Mixed Self and Random Mating with Applications to Genetic Conservation of Species. Crop Science, 39(5):1282--1294, 1999.Google ScholarCross Ref
- J. Wang. Effective population size under random mating with a finite number of matings. Philosophical Transactions of the Royal Society - B, 360:1395--1409, 2005.Google Scholar
- M. T. Wasan. Stochastic Approximation. Cambridge University Press, Cambridge, 1969.Google Scholar
- S. Wright. Evolution in mendelian populations. Genetics, 16:97--159, 1931.Google ScholarCross Ref
- S. Yang. Non-stationary problems optimization using the primal-dual genetic algorithm. In Congr. Evol. Comput., pages 2246--2253, 2003.Google Scholar
- S. Yang and R. Tinós. A hybrid immigrants scheme for genetic algorithms in dynamic environments. International Journal of Automation and Computing, 4(3):243--254, 2007.Google ScholarCross Ref
Index Terms
- Adaptive evolutionary algorithm based on population dynamics for dynamic environments
Recommendations
A population dynamics model to describe gene frequencies in evolutionary algorithms
The performance of evolutionary algorithms (EAs) may heavily depend severely on a suitable choice of parameters such as mutation and crossover rates. Several methods to adjust those parameters have been developed in order to enhance EA performance. For ...
Evolutionary and population dynamics of 3 parents differential evolution (3PDE) using self-adaptive tuning methodologies
Differential Evolution is known for its simplicity and effectiveness as an evolutionary optimizer. In recent years, many researchers have focused on the exploration of Differential Evolution (DE). The objective of this paper is to show the evolutionary ...
Grid Diversity Operator for Some Population-Based Optimization Algorithms
GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary ComputationWe present a novel diversity method named Grid Diversity Operator (GDO) that can be incorporated into multiple population-based optimization algorithms that guides the containing algorithm in creating new individuals in sparsely visited areas of the ...
Comments