A fuzzy-based lifetime extension of genetic algorithms

In knowledge discovery, Genetic Algorithms have been used for classification, model selection, and other optimization tasks. However, behavior and performance of genetic algorithms are directly affected by the values of their input parameters, while poor parameter settings usually lead to several problems such as the premature convergence. Adaptive techniques have been suggested for adjusting the parameters in the process of running the genetic algorithm. None of these techniques have yet shown a significant overall improvement, since most of them remain domain-specific. In this paper, we attempt to improve the performance of genetic algorithms by providing a new, fuzzy-based extension of the LifeTime feature. We use a Fuzzy Logic Controller (FLC) to adapt the crossover probability as a function of the chromosomes' age. The general principle is that for both young and old individuals the crossover probability is naturally low, while there is a certain age interval, where this probability is high. The concepts of ''young'', ''old'', and ''middle-aged'' are modeled as linguistic variables. This approach should enhance the exploration and exploitation capabilities of the algorithm, while reducing its rate of premature convergence. We have evaluated the proposed Lifetime methodology on several benchmark problems by comparing its performance to the basic genetic algorithm and to several adaptive genetic algorithms. The results of our initial experiments demonstrate a clear advantage of the fuzzy-based Lifetime extension over the ''crisp'' techniques.