Conference Paper

The Effect of Building Block Construction on the Behavior of the GA in Dynamic Environments: A Case Study Using the Shaky Ladder Hyperplane-Defined Functions.

DOI: 10.1007/11732242_75 Conference: Applications of Evolutionary Computing, EvoWorkshops 2006: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, and EvoSTOC, Budapest, Hungary, April 10-12, 2006, Proceedings
Source: DBLP

ABSTRACT The shaky ladder hyperplane-defined functions (sl-hdf's) are a test suite utilized for exploring the behavior of the genetic algorithm (GA) in dynamic environments. We present three ways of constructing the sl-hdf's by manipulating the way building blocks are constructed, combined, and changed. We examine the eect of the length of elemen- tary building blocks used to create higher building blocks, and the way in which those building blocks are combined. We show that the eects of building block construction on the behavior of the GA are complex. Our results suggest that construction routines which increase the roughness of the changes in the environment allow the GA to perform better by preventing premature convergence. Moreover, short length elementary building blocks permit early rapid progress.

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    ABSTRACT: One argument as to why the hyperplane-defined functions (hdf's) are a good testbed for the genetic algorithm (GA) is that the hdf's are built in the same way that the GA works. In this paper we test that hypothesis in a new setting by ex-ploring the GA on a subset of the hdf's which are dynamic, the shaky ladder hyperplane-defined functions (sl-hdf'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's. We then conduct a series of experiments with various crossover rates and var-ious rates of environmental change. Our results show that the GA performs better with than without crossover in dy-namic 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). More-over we show that crossover is even more beneficial in dy-namic 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.

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Sep 4, 2014