Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The evolutionary algorithm stochastic process is ll-known to be#R##N#Markovian. These have been under investigation in much of the#R##N#theoretical evolutionary computing research. When mutation rate is#R##N#positive, the Markov chain modeling an evolutionary algorithm is#R##N#irreducible and, therefore, has a unique stationary distribution,#R##N#yet, rather little is known about the stationary distribution. On the other#R##N#hand, knowing the stationary distribution may provide#R##N#some information about the expected times to hit optimum, assessment of the biases due to recombination and is of importance in population#R##N#genetics to assess what's called a ``genetic load" (see the#R##N#introduction for more details). In this talk I will show how the quotient#R##N#construction method can be exploited to derive rather explicit bounds on the ratios of the stationary distribution values of various subsets of#R##N#the state space. In fact, some of the bounds obtained in the current#R##N#work are expressed in terms of the parameters involved in all the#R##N#three main stages of an evolutionary algorithm: namely selection,#R##N#recombination and mutation. I will also discuss the nest developments which may allow for further improvements of the bounds
['Boris Mitavskiy', 'C. Cannings']
I am writing a research paper on a idea Evaluating Stationary Distribution of the Binary GA Markov Chain in Special Cases.
[{"from": "human", "value": "I am writing a research paper on a idea Evaluating Stationary Distribution of the Binary GA Markov Chain in Special Cases."}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The evolutionary algorithm stochastic process is ll-known to be#R##N#Markovian. These have been under investigation in much of the#R##N#theoretical evolutionary computing research. When mutation rate is#R##N#positive, the Markov chain modeling an evolutionary algorithm is#R##N#irreducible and, therefore, has a unique stationary distribution,#R##N#yet, rather little is known about the stationary distribution. On the other#R##N#hand, knowing the stationary distribution may provide#R##N#some information about the expected times to hit optimum, assessment of the biases due to recombination and is of importance in population#R##N#genetics to assess what's called a ``genetic load\" (see the#R##N#introduction for more details). In this talk I will show how the quotient#R##N#construction method can be exploited to derive rather explicit bounds on the ratios of the stationary distribution values of various subsets of#R##N#the state space. In fact, some of the bounds obtained in the current#R##N#work are expressed in terms of the parameters involved in all the#R##N#three main stages of an evolutionary algorithm: namely selection,#R##N#recombination and mutation. I will also discuss the nest developments which may allow for further improvements of the bounds"}]