Interactive genetic algorithms (IGAs) are effective methods to solve optimization problems with implicit or fuzzy indices. But human fatigue problem, resulting from evaluation on individuals and assignment of their fitness, is very important and hard to solve in IGAs. Aiming at solving the above problem, an interactive genetic algorithm with an individual fitness not assigned by human is propos...

This work addresses the problem of finding the adjustable parameters of a learning algorithm using Genetic Algorithms. This problem is also known as the model selection problem. In this paper, some model selection techniques (e.g., crossvalidation and bootstrap) are used as objective functions of a Genetic Algorithm. The Genetic Algorithm is modified in order to allow the efficient use of these...

the bankruptcy prediction models have long been proposedas a key subject in finance. the present study, therefore, makes aneffort to examine the corporate bankruptcy prediction through employmentof the genetic algorithm model. furthermore, it attempts to evaluatethe strategies to overcome the drawbacks of ordinary methods forbankruptcy prediction through application of genetic algorithms. thesa...

there are many real problems in which multiple responses should be optimized simultaneously by setting of process variables. one of the common approaches for optimization of multi-response problems is desirability function. in most real cases, there is a correlation structure between responses so ignoring the correlation may lead to mistake results. hence, in this paper a robust approach based ...

In this study, we propose to use an Adaptive Genetic Algorithm (AGA) aimed to enhance the performance of information retrieval under Vector Space Model (VSM) in both (Cosine and Dice similarity). Using the algorithm is aimed to improve the quality of the results of user's query and generate improved queries that fit searcher’s needs. Furthermore, we investigate and evaluate different fitness fu...

Data envelopment analysis (DEA) is a methodology for measuring the relative efficiency of decision making units (DMUs) which ‎consume the same types of inputs and producing the same types of outputs. Believing that future planning and predicting the ‎efficiency are very important for DMUs, this paper first presents a new dynamic random fuzzy DEA model (DRF-DEA) with ‎common weights (using...

The evolutionary multiobjective optimization technique for analog circuit optimizer is presented in this paper. the technique uses a Parallel Genetic Algorithm(PGA) to identifies multiple “good” solutions from a multiobjective fitness landscape which are tuned using a local hill-climbing algorithm. The PGA is used to provide a nature niching mechanism that has considerable computational advanta...

The aim of this study is to optimize performance functions of turbofan engines considering the off-design model of turbofan engine as well as employing multi-objective genetic algorithm. The design variables including high-pressure compressor pressure ratio, low-pressure compressor pressure ratio, fan pressure ratio and bypass ratio are calculated in such a way that the corresponding functions ...

Establishing suitable groups of students is one the factors considered as a key to success in group collaboration. In addition, searching for optimal solution problem can be more complicated and becomes an exhaustive search, while taking into consideration equality homogeneity. However, few approaches focus on forming based analysis variance (ANOVA) ensure that all generated have been drawn fro...