An introduction to genetic algorithms
An introduction to genetic algorithms
Calculating the expected loss of diversity of selection schemes
Evolutionary Computation
A Mathematical Analysis of Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
A markov chain framework for the simple genetic algorithm
Evolutionary Computation
A comparison of selection schemes used in evolutionary algorithms
Evolutionary Computation
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Backward-chaining genetic programming
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Backward-chaining evolutionary algorithms
Artificial Intelligence
Another investigation on tournament selection: modelling and visualisation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Genetic Programming and Evolvable Machines
Backward-chaining evolutionary algorithms
Artificial Intelligence
A survey and taxonomy of performance improvement of canonical genetic programming
Knowledge and Information Systems
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Tournament selection performs tournaments by first sampling individuals uniformly at random from the population and then selecting the best of the sample for some genetic operation. This sampling process needs to be repeated many times when creating a new generation. However, even upon iteration, it may happen not to sample some of the individuals in the population. These individuals can therefore play no role in future generations. Under conditions of low selection pressure, the fraction of individuals not involved in any way in the selection process may be substantial. In this paper we investigate how we can model this process and we explore the possibility, methods and consequences of not generating and evaluating those individuals with the aim of increasing the efficiency of evolutionary algorithms based on tournament selection. In some conditions, considerable savings in terms of fitness evaluations are easily achievable, without altering in any way the expected behaviour of such algorithms.