The theory of evolution strategies
The theory of evolution strategies
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators
Artificial Intelligence Review
The parameter-less genetic algorithm in practice
Information Sciences—Informatics and Computer Science: An International Journal
Goal-oriented preservation of essential genetic information by offspring selection
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Schemata evolution and building blocks
Evolutionary Computation
Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications
Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications
Self-adaptive population size adjustment for genetic algorithms
EUROCAST'07 Proceedings of the 11th international conference on Computer aided systems theory
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This paper exemplarily points out how essential genetic information evolves during the runs of certain selected GA-variants. The discussed algorithmic enhancements to a standard genetic algorithm certify the survival of essential genetic information by supporting the survival of relevant alleles rather than the survival of above average chromosomes. This is achieved by defining the survival probability of a new child chromosome depending on the child's fitness in comparison to the fitness values of its own parents. The described kind of analysis assumes the knowledge of the unique global optimal solution and is therefore restricted to rather theoretical considerations The main aim of this paper is to explain the most important properties of the discussed algorithm variants in a rather intuitive way. Aspects for meaningful and practically more relevant generalizations as well as more sophisticated experimental analyses are indicated.