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
Modeling Building-Block Interdependency
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
On the Analysis of Evolutionary Algorithms - A Proof That Crossover Really Can Help
ESA '99 Proceedings of the 7th Annual European Symposium on Algorithms
Ignoble Trails - Where Crossover Is Provably Harmful
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Just what are building blocks?
FOGA'07 Proceedings of the 9th international conference on Foundations of genetic algorithms
The benefit of migration in parallel evolutionary algorithms
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Theoretical results in genetic programming: the next ten years?
Genetic Programming and Evolvable Machines
Practical performance models of algorithms in evolutionary program induction and other domains
Artificial Intelligence
Benefits of a population: five mechanisms that advantage population-based algorithms
IEEE Transactions on Evolutionary Computation
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
An analysis on recombination in multi-objective evolutionary optimization
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Crossover can provably be useful in evolutionary computation
Theoretical Computer Science
The use of tail inequalities on the probable computational time of randomized search heuristics
Theoretical Computer Science
Runtime analysis of convex evolutionary search
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Crossover speeds up building-block assembly
Proceedings of the 14th annual conference on Genetic and evolutionary computation
On the analysis of the simple genetic algorithm
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Impact of different recombination methods in a mutation-specific MOEA for a biochemical application
EvoBIO'13 Proceedings of the 11th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
A variance decomposition approach to the analysis of genetic algorithms
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Improved runtime analysis of the simple genetic algorithm
Proceedings of the 15th annual conference on Genetic and evolutionary computation
An analysis on recombination in multi-objective evolutionary optimization
Artificial Intelligence
Runtime analysis of the (1+1) EA on computing unique input output sequences
Information Sciences: an International Journal
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Mutation and crossover are the main search operators of different variants of evolutionary algorithms. Despite the many discussions on the importance of crossover nobody has proved rigorously for some explicitly defined fitness functions f"n:{0,1}^n-R that a genetic algorithm with crossover can optimize f"n in expected polynomial time while all evolution strategies based only on mutation (and selection) need expected exponential time. Here such functions and proofs are presented for a genetic algorithm without any idealization. For some functions one-point crossover is appropriate while for others uniform crossover is the right choice.