Generalizing the notion of schema in genetic algorithms
Artificial Intelligence
Computer
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
How Genetic Algorithms Work: A Critical Look at Implicit Parallelism
Proceedings of the 3rd International Conference on Genetic Algorithms
Optimal Mutation Rates in Genetic Search
Proceedings of the 5th International Conference on Genetic Algorithms
Strategy Adaption by Competing Subpopulations
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Predictive models for the breeder genetic algorithm i. continuous parameter optimization
Evolutionary Computation
Toward a theory of evolution strategies: Some asymptotical results from the (1,+ λ)-theory
Evolutionary Computation
A derandomized approach to self-adaptation of evolution strategies
Evolutionary Computation
Toward a theory of evolution strategies: The (μ, λ)-theory
Evolutionary Computation
Toward a theory of evolution strategies: Self-adaptation
Evolutionary Computation
Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
Artificial Intelligence Review
Reference frame and scale invariant real-parameter genetic and differential evolution algorithms
Proceedings of the 9th annual conference on Genetic and evolutionary computation
| Hi-index | 0.00 |
The development of a sound theory that predicts and verifies existing evolutionary algorithms (EA) is one of the most important research issues in the field today. In mathematical proofs, the assumption of spherical symmetry is probably one of the most widely used simplifications. This paper discusses the extent to which spherical symmetry is appropriate for certain EAs. It turns out that spherical symmetry leads to simplifications in (self-adaptive) EAs but seems inappropriate for certain genetic algorithm variants, since small mutation rates bias a search algorithm toward the coordinate axes. This paper also argues that current test suites are weak in that they do not provide problems with significant epistasis that describes the interaction between different parameters. Consequently, when using an empirical test for pushing existing theory beyond its limits, benchmark functions should include more epistatic interaction or at least should use coordinate rotations.