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Runtime Analysis of Fitness-Proportionate Selection on Linear Functions
In contrast to this negative result, we also show that for any linear function with polynomially bounded weights, the EA achieves a polynomial expected runtime if the mutation rate is reduced to $\Theta(1/n^2)$ and the population size is sufficiently large.
Modularity in biological evolution and evolutionary computation
One of the main properties of biological systems is modularity, which manifests itself at all levels of their organization, starting with the level of molecular genetics, ending with the level of whole organisms and their communities.
Hitting times of local and global optima in genetic algorithms with very high selection pressure
The paper is devoted to upper bounds on the expected first hitting times of the sets of local or global optima for non-elitist genetic algorithms with very high selection pressure.
On Proportions of Fit Individuals in Population of Evolutionary Algorithm with Tournament Selection
In this paper, we consider a fitness-level model of a non-elitist mutation-only evolutionary algorithm (EA) with tournament selection.
Non-Elitist Genetic Algorithm as a Local Search Method
Sufficient conditions are found under which the iterated non-elitist genetic algorithm with tournament selection first visits a local optimum in polynomially bounded time on average.
