@conference {16425,
	title = {The defined cliffs variant in dynamic environments: a case study using the shaky ladder hyperplane-defined functions},
	booktitle = {Proceedings of the 9th annual conference on Genetic and evolutionary computation},
	series = {GECCO {\textquoteright}07},
	year = {2007},
	month = {2007///},
	pages = {1158 - 1164},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {The shaky ladder hyperplane-defined functions (sl-hdfs) are a test suite utilized for exploring the behavior of the genetic algorithm (GA) in dynamic environments. This test suite can generate arbitrary problems with similar levels of difficulty and it provides a platform for systematic controlled observations of the GA in dynamic environments. Previous work has found two factors that contribute to the GA{\textquoteright}s success on sl-hdfs: (1) short initial building blocks and (2) significantly changing the reward structure during fitness landscape changes. Therefore a test function that combines these two features should facilitate even better GA performance. This has led to the construction of a new sl-hdf variant, "Defined Cliffs," in which we combine short elementary building blocks with sharp transitions in the environment. We examine this variant with two different levels of dynamics, static and regularly changing, using four different metrics. The results show superior GA performance on the Defined Cliffs over all previous variants (Cliffs, Weight, and Smooth). Our observations and conclusions in this variant further the understanding of the GA in dynamic environments.},
	keywords = {building blocks, dynamic environments, Genetic algorithms, shaky ladder hyperplane-defined functions},
	isbn = {978-1-59593-697-4},
	doi = {10.1145/1276958.1277186},
	url = {http://doi.acm.org/10.1145/1276958.1277186},
	author = {Alharbi,Abir and Rand, William and Riolo,Rick}
}
@conference {16423,
	title = {The effect of crossover on the behavior of the GA in dynamic environments: a case study using the shaky ladder hyperplane-defined functions},
	booktitle = {Proceedings of the 8th annual conference on Genetic and evolutionary computation},
	series = {GECCO {\textquoteright}06},
	year = {2006},
	month = {2006///},
	pages = {1289 - 1296},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {One argument as to why the hyperplane-defined functions (hdf{\textquoteright}s) are a good testbed for the genetic algorithm (GA) is that the hdf{\textquoteright}s are built in the same way that the GA works. In this paper we test that hypothesis in a new setting by exploring the GA on a subset of the hdf{\textquoteright}s which are dynamic---the shaky ladder hyperplane-defined functions (sl-hdf{\textquoteright}s). In doing so we gain insight into how the GA makes use of crossover during its traversal of the sl-hdf search space. We begin this paper by explaining the sl-hdf{\textquoteright}s. We then conduct a series of experiments with various crossover rates and various rates of environmental change. Our results show that the GA performs better with than without crossover in dynamic environments. Though these results have been shown on some static functions in the past, they are re-confirmed and expanded here for a new type of function (the hdf) and a new type of environment (dynamic environments). Moreover we show that crossover is even more beneficial in dynamic environments than it is in static environments. We discuss how these results can be used to develop a richer knowledge about the use of building blocks by the GA.},
	keywords = {crossover, dynamic environments, Genetic algorithms, hyperplane-defined functions, search},
	isbn = {1-59593-186-4},
	doi = {10.1145/1143997.1144198},
	url = {http://doi.acm.org/10.1145/1143997.1144198},
	author = {Rand, William and Riolo,Rick and Holland,John H.}
}
@conference {16420,
	title = {Measurements for understanding the behavior of the genetic algorithm in dynamic environments: a case study using the Shaky Ladder Hyperplane-Defined Functions},
	booktitle = {Proceedings of the 2005 workshops on Genetic and evolutionary computation},
	series = {GECCO {\textquoteright}05},
	year = {2005},
	month = {2005///},
	pages = {32 - 38},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {We describe a set of measures to examine the behavior of the Genetic Algorithm (GA) in dynamic environments. We describe how to use both average and best measures to look at performance, satisficability, robustness, and diversity. We use these measures to examine GA behavior with a recently devised dynamic test suite, the Shaky Ladder Hyperplane-Defined Functions (sl-hdf{\textquoteright}s). This test suite can generate random problems with similar levels of difficulty and provides a platform allowing systematic controlled observations of the GA in dynamic environments. We examine the results of these measures in two different versions of the sl-hdf{\textquoteright}s, one static and one regularly-changing. We provide explanations for the observations in these two different environments, and give suggestions as to future work.},
	keywords = {dynamic environments, Genetic algorithms, hyperplane-defined functions, measurement},
	doi = {10.1145/1102256.1102263},
	url = {http://doi.acm.org/10.1145/1102256.1102263},
	author = {Rand, William and Riolo,Rick}
}
@conference {16419,
	title = {The problem with a self-adaptative mutation rate in some environments: a case study using the shaky ladder hyperplane-defined functions},
	booktitle = {Proceedings of the 2005 conference on Genetic and evolutionary computation},
	series = {GECCO {\textquoteright}05},
	year = {2005},
	month = {2005///},
	pages = {1493 - 1500},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Dynamic environments have periods of quiescence and periods of change. In periods of quiescence a Genetic Algorithm (GA) should (optimally) exploit good individuals while in periods of change the GA should (optimally) explore new solutions. Self-adaptation is a mechanism which allows individuals in the GA to choose their own mutation rate, and thus allows the GA to control when it explores new solutions or exploits old ones. We examine the use of this mechanism on a recently devised dynamic test suite, the Shaky Ladder Hyperplane-Defined Functions (sl-hdf{\textquoteright}s). This test suite can generate random problems with similar levels of difficulty and provides a platform allowing systematic controlled observations of the GA in dynamic environments. We show that in a variety of circumstances self-adaptation fails to allow the GA to perform better on this test suite than fixed mutation, even when the environment is static. We also show that mutation is beneficial throughout the run of a GA, and that seeding a population with known good genetic material is not always beneficial to the results. We provide explanations for these observations, with particular emphasis on comparing our results to other results [2] which have shown the GA to work in static environments. We conclude by giving suggestions as to how to change the simple GA to solve these problems.},
	keywords = {dynamic environments, Genetic algorithms, hyperplane-defined functions, self-adaptation},
	isbn = {1-59593-010-8},
	doi = {10.1145/1068009.1068245},
	url = {http://doi.acm.org/10.1145/1068009.1068245},
	author = {Rand, William and Riolo,Rick}
}
