Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Randomized algorithms
An introduction to genetic algorithms
An introduction to genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
IEEE Transactions on Evolutionary Computation
The analysis of a recombinative hill-climber on H-IFF
IEEE Transactions on Evolutionary Computation
Crossover is provably essential for the Ising model on trees
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Crossover can provably be useful in evolutionary computation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Theoretical analysis of diversity mechanisms for global exploration
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Population size versus runtime of a simple evolutionary algorithm
Theoretical Computer Science
Ignoble Trails - Where Crossover Is Provably Harmful
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Crossover Can Be Constructive When Computing Unique Input Output Sequences
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Analysis of diversity-preserving mechanisms for global exploration*
Evolutionary Computation
Theoretical results in genetic programming: the next ten years?
Genetic Programming and Evolvable Machines
Towards analyzing recombination operators in evolutionary search
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
How crossover helps in pseudo-boolean optimization
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Crossover can provably be useful in evolutionary computation
Theoretical Computer Science
Crossover speeds up building-block assembly
Proceedings of the 14th annual conference on Genetic and evolutionary computation
A runtime analysis of simple hyper-heuristics: to mix or not to mix operators
Proceedings of the twelfth workshop on Foundations of genetic algorithms XII
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Runtime analysis of the (1+1) EA on computing unique input output sequences
Information Sciences: an International Journal
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Evolutionary and genetic algorithms (EAs and GAs) are quite successful randomized function optimizers. This success is mainly based on the interaction of different operators like selection, mutation, and crossover. Since this interaction is still not well understood, one is interested in the analysis of the single operators. Jansen and Wegener [Proceedings of GECCO'2001, 2001, pp. 375-382] have described so-called real royal road functions where simple steady-state GAs have a polynomial expected optimization time while the success probability of mutation-based EAs is exponentially small even after an exponential number of steps. This success of the GA is based on the crossover operator and a population whose size is moderately increasing with the dimension of the search space. Here new real royal road functions are presented where crossover leads to a small optimization time, although the GA works with the smallest possible population size--namely 2.