Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
What Makes a Problem Hard for a Genetic Algorithm? Some Anomalous Results and Their Explanation
Machine Learning - Special issue on genetic algorithms
An introduction to genetic algorithms
An introduction to genetic algorithms
Statistical dynamics of the Royal Road genetic algorithm
Theoretical Computer Science - Special issue on evolutionary computation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
GA Performance in a Babel-like Fitness Landscape
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
Simple genetic algorithms with linear fitness
Evolutionary Computation
A description of holland's royal road function
Evolutionary Computation
A comparison of the fixed and floating building block representation in the genetic algorithm
Evolutionary Computation
Genetic algorithms and artificial life
Artificial Life
Biologically-implemented genetic algorithm for protein engineering
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
On the movement of vertex fixed points in the simple GA
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
Automated operator selection on genetic algorithms
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
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The effectiveness of crossover in accelerating evolution in genetic algorithms (GAs) is studied with a haploid finite population of bit sequences. A Babel-like fitness landscape is assumed. There is a single bit sequence (schema) that is significantly more advantageous than all the others. We study the time until domination of the advantageous schema (Td). Evolution proceeds with appearance, spread, and domination of the advantageous schema. The most important process determining Td is the appearance (creation) of the advantageous schema. Crossover helps this creation process and enhances the rate of evolution. To study this effect, we first establish an analytical method to estimate Td with or without crossover. Then, we conduct a numerical analysis using the frequency vector representation of the population with the recurrence relations formulated after GA operations. Finally, we carry out direct computer simulations with simple GAs operating on a population of binary strings directly prepared in the computer memory to examine the performance of the two analytical methods. It is shown that Td is reduced greatly by crossover with a mildly high rate when the mutation rate is adjusted to a moderate value and that an advantageous schema has a fairly large order (the number of bits). From these observations, we can determine implementation criteria for GAs, which are useful when we applying GAs to engineering problems having a conspicuously discontinuous fitness landscape.