Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
An adaptive crossover distribution mechanism for genetic algorithms
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Nonstationary function optimization using genetic algorithm with dominance and diploidy
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Evolving artificial intelligence
Evolving artificial intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms
Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Evolutionary Programming IV: Proceedings of the Fourth Annual Conference on Evolutionary Programming
Evolutionary Programming IV: Proceedings of the Fourth Annual Conference on Evolutionary Programming
Parallel Problem Solving from Nature, 2: Proceedings of the Second Conference on Parallel Problem Solving from Nature, Brussels, Belgium, 28-30 September, 1992
Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Optimal Mutation Rates in Genetic Search
Proceedings of the 5th International Conference on Genetic Algorithms
RapidAccurate Optimization of Difficult Problems Using Fast Messy Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
A Variant of Evolution Strategies for Vector Optimization
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Step-Size Adaption Based on Non-Local Use of Selection Information
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
An Evolutionary Algorithm for Integer Programming
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Are Evolutionary Algorithms Improved by Large Mutations?
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Intelligent Mutation Rate Control in Canonical Genetic Algorithms
ISMIS '96 Proceedings of the 9th International Symposium on Foundations of Intelligent Systems
An Analysis of Evolutionary Algorithms Based on Neighborhood and Step Sizes
EP '97 Proceedings of the 6th International Conference on Evolutionary Programming VI
The behavior of adaptive systems which employ genetic and correlation algorithms
The behavior of adaptive systems which employ genetic and correlation algorithms
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Toward a theory of evolution strategies: Self-adaptation
Evolutionary Computation
On the Expected Runtime and the Success Probability of Evolutionary Algorithms
WG '00 Proceedings of the 26th International Workshop on Graph-Theoretic Concepts in Computer Science
Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms
Genetic Programming and Evolvable Machines
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The principle of self-adaptation in evolutionary algorithms is an important mechanism for controlling the strategy parameters of such algorithms by evolving parameter values in analogy with the usual evolution of object variables. To facilitate evolution of strategy parameters, they are incorporated into the representation of individuals and are subject to the evolutionary variation operators in a similar way as the object variables. This survey paper provides an overview of the existing techniques for the self-adaptation of strategy parameters related to mutation and recombination operators, indicating that the principle works under a variety of conditions regarding the search space of the underlying optimization problem and the method used for the variation of strategy parameters. Although a number of open questions remain, self-adaptation is identified as a generally applicable, robust and efficient method for parameter control in evolutionary algorithms.