On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
A study of drift analysis for estimating computation time of evolutionary algorithms
Natural Computing: an international journal
Real royal road functions for constant population size
Theoretical Computer Science
Crossover is provably essential for the Ising model on trees
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
The one-dimensional Ising model: mutation versus recombination
Theoretical Computer Science
Upper and Lower Bounds for Randomized Search Heuristics in Black-Box Optimization
Theory of Computing Systems
A building-block royal road where crossover is provably essential
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
Ignoble Trails - Where Crossover Is Provably Harmful
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Improved analysis methods for crossover-based algorithms
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Running Time Analysis of ACO Systems for Shortest Path Problems
SLS '09 Proceedings of the Second International Workshop on Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics
Analysis of diversity-preserving mechanisms for global exploration*
Evolutionary Computation
Real royal road functions-where crossover provably is essential
Discrete Applied Mathematics - Special issue: Boolean and pseudo-boolean funtions
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Black-box search by unbiased variation
Proceedings of the 12th annual conference on Genetic and evolutionary computation
More effective crossover operators for the all-pairs shortest path problem
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity
Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity
Abstract convex evolutionary search
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
The analysis of a recombinative hill-climber on H-IFF
IEEE Transactions on Evolutionary Computation
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
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
Reducing the arity in unbiased black-box complexity
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
More effective crossover operators for the all-pairs shortest path problem
Theoretical Computer Science
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
Lessons from the black-box: fast crossover-based 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
| Hi-index | 0.00 |
Understanding the impact of crossover on performance is a major problem in the theory of genetic algorithms (GAs). We present new insight on working principles of crossover by analyzing the performance of crossover-based GAs on the simple functions OneMax and Jump. First, we assess the potential speedup by crossover when combined with a fitness-invariant bit shuffling operator that simulates a lineage of independent evolution on a function of unitation. Theoretical and empirical results show drastic speedups for both functions. Second, we consider a simple GA without shuffling and investigate the interplay of mutation and crossover on Jump. If the crossover probability is small, subsequent mutations create sufficient diversity, even for very small populations. Contrarily, with high crossover probabilities crossover tends to lose diversity more quickly than mutation can create it. This has a drastic impact on the performance on Jump. We complement our theoretical findings by Monte Carlo simulations on the population diversity.