30-07-2012, 03:47 PM
A fuzzy-based lifetime extension of genetic algorithms
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Introduction
principles of the natural evolution [13,19]. However, behavior and performance of genetic algorithms are
directly affected by the interaction between their parameters [7], which have fixed values in the Simple
Genetic Algorithm [18]. Poor parameter settings usually lead to several problems such as premature
convergence.
Adaptive techniques have been suggested to adjust parameters in the process of running the genetic
algorithm. These include adapting mutation probability (Pm), crossover probability (Pc), both Pm and
Pc, population size (N) [1,8,28], and even the crossover operator [27]. Lately, several features have been
added to the basic genetic algorithm in order to emulate the natural evolution process as precisely as
possible. Particularly, the approach called GAVaPS - GA with varying population size [2], which utilizes
the concept of LifeTime and age, has shown promising results. Further studies enhancing the basic
GAVaPS took place in [10,11] combining known features like incest prohibition and mating between
individuals with phenotype similarities.
Recently, there has been an increasing interest in the integration of Fuzzy Logic (FL) and Genetic
Algorithms (GAs). The motivation is to controlGAparameters based on fuzzy logic techniques [14,15,17].
Currently, experience and knowledge on GA have become available as a result of empirical studies, which
may be useful for avoiding premature convergence and improving GA behavior. However, too much of
this information is vague, incomplete or ill structured which causes it to be rarely applied. Therefore, the
use of fuzzy logic would be suitable for dealing with this type of information. The good performance of
the FGA (Fuzzy GA) approaches leads to an important conclusion: GAs may be improved through the
use of FL.
Conclusions and future work
This paper presented the Fuzzy-based lifetime extension of GA, which enables the use of ill-structured
experience and vague information, as well as the human intuition.We used this capability to improve the
emulation of the biological process by dynamically adapting the crossover probability. However, other
parameters, preferable to those derived from nature, may be modeled as well.
The Fuzzy-based lifetime extension of GA outperformed the Simple GA, theAGA, and the GAVaPS in
all case studies: at least one fuzzy-based configuration reached better results in fewer generations.With
a fixed number of generations, it reached higher fitness than GAVaPS in two cases out of three, while in
the OneMax problem it was only slightly worse than GAVaPS. One explanation is that in some problems,
there exists a relationship between the membership function and the objective function. Logically, it is
possible that with a suitable membership function the lifetime extended GA would perform better than
GAVaPS in the third case study as well, but it is an open issue to be explored. Within the fuzzy-based
lifetime extension of GA, the first configuration, which defines up to 30% as young and 70% as old,
was found to be preferable most of the times, which seems reasonable when we consider the biological
process. Still, the study of these parameters, as well as the settings of the “expert knowledge”, requires
further research and experimentation.