Proceedings of the third international conference on Genetic algorithms
The evolution of evolvability in genetic programming
Advances in genetic programming
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Proceedings of the 3rd International Conference on Genetic Algorithms
Optimal Mutation Rates in Genetic Search
Proceedings of the 5th International Conference on Genetic Algorithms
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Combinatorial Algorithms: Theory and Practice
Combinatorial Algorithms: Theory and Practice
Artificial Life
Experiments with Tuneable Fitness Landscapes
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Smoothness, ruggedness and neutrality of fitness landscapes: from theory to application
Advances in evolutionary computing
Variable discrimination of crossover versus mutation using parameterized modular structure
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A building-block royal road where crossover is provably essential
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Algebraic theory of recombination spaces
Evolutionary Computation
Genetic algorithms, path relinking, and the flowshop sequencing problem
Evolutionary Computation
On the futility of blind search: An algorithmic view of “no free lunch”
Evolutionary Computation
Fitness landscapes and graphs: multimodularity, ruggedness and neutrality
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Approaches to multidimensional scaling for adaptive landscape visualization
Proceedings of the 12th annual conference on Genetic and evolutionary computation
An empirical approach to the measurement of interchromosomal distances in the genetic algorithm
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Depictions of genotypic space for evaluating the suitability of different recombination operators
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Fitness landscapes and graphs: multimodularity, ruggedness and neutrality
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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We compare the search power of crossover and mutation in genetic algorithms. Our discussion is framed within a model of computation using search space structures induced by these operators. Isomorphisms between the search spaces generated by these operators on small populations are identified and explored. These are closely related to the binary reflected Gray code. Using these we generate discriminating functions that are hard for one operator but easy for the other and show how to transform from one case to the other. We use these functions to provide theoretical evidence that traditional GAs use mutation more effectively than crossover, but dispute claims that mutation is a better search mechanism than crossover. To the contrary, we show that methods that exploit crossover more effectively can be designed and give evidence that these are powerful search mechanisms. Experimental results using GIGA, the Gene Invariant Genetic Algorithm, and the well-known GENESIS program support these theoretical claims. Finally, this paper provides the initial approach to a different method of analysis of GAs that does not depend on schema analysis or the notions of increased allocations of trials to hyperplanes of above-average fitness. Instead it focuses on the search space structure induced by the operators and the effect of a population search using them.