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
Randomized algorithms
A solvable model of a hard optimisation problem
Theoretical aspects of evolutionary computing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
Bayesian optimization algorithm: from single level to hierarchy
Bayesian optimization algorithm: from single level to hierarchy
Real royal road functions: where crossover provably is essential
Discrete Applied Mathematics - Special issue: Boolean and pseudo-boolean funtions
Building Blocks, Cohort Genetic Algorithms, and Hyperplane-Defined Functions
Evolutionary Computation
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Mutation-crossover isomorphisms and the construction of discriminating functions
Evolutionary Computation
The simple genetic algorithm and the walsh transform: Part i, theory
Evolutionary Computation
On the unit of selection in sexual populations
ECAL'05 Proceedings of the 8th European conference on Advances in Artificial Life
The analysis of a recombinative hill-climber on H-IFF
IEEE Transactions on Evolutionary Computation
Variable discrimination of crossover versus mutation using parameterized modular structure
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Computational complexity and evolutionary computation
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
On the performance effects of unbiased module encapsulation
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Computational complexity and evolutionary computation
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
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
Symbiosis, synergy and modularity: introducing the reciprocal synergy symbiosis algorithm
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
The benefit of migration in parallel evolutionary algorithms
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Computational complexity and evolutionary computation
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Benefits of a population: five mechanisms that advantage population-based algorithms
IEEE Transactions on Evolutionary Computation
Learning the large-scale structure of the MAX-SAT landscape using populations
IEEE Transactions on Evolutionary Computation
How crossover helps in pseudo-boolean optimization
Proceedings of the 13th annual conference on Genetic and evolutionary computation
On the effectiveness of crossover for migration in parallel evolutionary algorithms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Can selfish symbioses effect higher-level selection?
ECAL'09 Proceedings of the 10th European conference on Advances in artificial life: Darwin meets von Neumann - Volume Part II
Symbiosis enables the evolution of rare complexes in structured environments
ECAL'09 Proceedings of the 10th European conference on Advances in artificial life: Darwin meets von Neumann - Volume Part II
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Convergence of a recombination-based elitist evolutionary algorithm on the royal roads test function
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
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
Homogeneous and heterogeneous island models for the set cover problem
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Improved runtime analysis of the simple genetic algorithm
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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One of the most controversial yet enduring hypotheses about what genetic algorithms (GAs) are good for concerns the idea that GAs process building-blocks. More specifically, it has been suggested that crossover in GAs can assemble short low-order schemata of above average fitness (building blocks) to create higher-order higher-fitness schemata. However, there has been considerable difficulty in demonstrating this rigorously and intuitively. Here we provide a simple building-block function that a GA with two-point crossover can solve on average in polynomial time, whereas an asexual population or mutation hill-climber cannot.