Uniform crossover in genetic algorithms
Proceedings of the third international conference on Genetic algorithms
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Evaluating evolutionary algorithms
Artificial Intelligence - Special volume on empirical methods
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Optimal Mutation and Crossover Rates for a Genetic Algorithm Operating in a Dynamic Environment
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Building Blocks, Cohort Genetic Algorithms, and Hyperplane-Defined Functions
Evolutionary Computation
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Controlled observations of the genetic algorithm in a changing environment: case studies using the shaky ladder hyperplane-defined functions
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Sim-paramecium algorithm based on enhanced livability and competition
ICS'09 Proceedings of the 13th WSEAS international conference on Systems
Adaptive life-cycle and viability based paramecium-imitated evolutionary algorithm
WSEAS Transactions on Computers
The effect of varying the crossover rate in the evolution of bidding strategies
ACST '08 Proceedings of the Fourth IASTED International Conference on Advances in Computer Science and Technology
| Hi-index | 0.00 |
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 exploring 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 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.