Finite Markov chain analysis of genetic algorithms
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
An analysis of the effects of selection in genetic algorithms
An analysis of the effects of selection in genetic algorithms
Proceedings of the third international conference on Genetic algorithms
Information Processing Letters
A simpler derivation of schema hazard in genetic algorithms
Information Processing Letters
A branching process model for genetic algorithms
Information Processing Letters
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Adaptive Selection Methods for Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
How Genetic Algorithms Work: A Critical Look at Implicit Parallelism
Proceedings of the 3rd International Conference on Genetic Algorithms
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Sizing Populations for Serial and Parallel Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
A Markov Chain Analysis on A Genetic Algorithm
Proceedings of the 5th International Conference on Genetic Algorithms
Global Convergence of Genetic Algorithms: A Markov Chain Analysis
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
On the Mean Convergence Time of Evolutionary Algorithms without Selection and Mutation
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 analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
Predictive models for the breeder genetic algorithm i. continuous parameter optimization
Evolutionary Computation
A markov chain framework for the simple genetic algorithm
Evolutionary Computation
The science of breeding and its application to the breeder genetic algorithm (bga)
Evolutionary Computation
Selection in evolutionary algorithms for the traveling salesman problem
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
On Modelling Evolutionary Algorithm Implementations through Co-operating Populations
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
An analysis of Gray versus binary encoding in genetic search
Information Sciences: an International Journal - Special issue: Evolutionary computation
Takeover time curves in random and small-world structured populations
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
On Replacement Strategies in Steady State Evolutionary Algorithms
Evolutionary Computation
Rigorous hitting times for binary mutations
Evolutionary Computation
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
International Journal of Remote Sensing
Pair approximations of takeover dynamics in regular population structures
Evolutionary Computation
Minimising the delta test for variable selection in regression problems
International Journal of High Performance Systems Architecture
Selection pressure and takeover time of distributed evolutionary algorithms
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Solving the stochastic dynamic lot-sizing problem through nature-inspired heuristics
Computers and Operations Research
Research: An efficient link enhancement strategy for computer networks using genetic algorithm
Computer Communications
Analyzing the behaviour of population-based algorithms using rayleigh distribution
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
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A Markov chain framework is developed for analyzing a wide variety of selection techniques used in genetic algorithms (GAs) and evolution strategies (ESs). Specifically, we consider linear ranking selection, probabilistic binary tournament selection, deterministic s-ary (s = 3,4,...) tournament selection, fitness-proportionate selection, selection in Whitley's GENITOR, selection in (μ, λ)-ES, selection in (μ + λ)-ES, (μ, λ)-linear ranking selection in GAs, (μ + λ)-linear ranking selection in GAs, and selection in Eshelman's CHC algorithm. The analysis enables us to compare and contrast the various selection algorithms with respect to several performance measures based on the probability of takeover. Our analysis is exact---we do not make any assumptions or approximations. Finite population sizes are considered. Our approach is perfectly general, and following the methods of this paper, it is possible to analyze any selection strategy in evolutionary algorithms.