Evolutionary Algorithms for Fuzzy Cognitive Maps

Fuzzy Cognitive Maps (FCMs) is a complex systems modeling technique which, due to its unique advantages, has lately risen in popularity. They are based on graphs that represent the causal relationships among the parameters of the system to be modeled, and they stand out for their interpretability and flexibility. With the late popularity of FCMs, a plethora of research efforts have taken place to develop and optimize the model. One of the most important elements of FCMs is the learning algorithm they use, and their effectiveness is largely determined by it. The learning algorithms learn the node weights of an FCM, with the goal of converging towards the desired behavior. The present study reviews the genetic algorithms used for training FCMs, as well as gives a general overview of the FCM learning algorithms, putting evolutionary computing into the wider context.Comment: 20 page

Fuzzy logic based approaches for gene regulatory network inference

The rapid advancement in high-throughput techniques has fueled the generation of large volume of biological data rapidly with low cost. Some of these techniques are microarray and next generation sequencing which provides genome level insight of living cells. As a result, the size of most of the biological databases, such as NCBI-GEO, NCBI-SRA, is exponentially growing. These biological data are analyzed using computational techniques for knowledge discovery - which is one of the objectives of bioinformatics research. Gene regulatory network (GRN) is a gene-gene interaction network which plays pivotal role in understanding gene regulation process and disease studies. From the last couple of decades, the researchers are interested in developing computational algorithms for GRN inference (GRNI) using high-throughput experimental data. Several computational approaches have been applied for inferring GRN from gene expression data including statistical techniques (correlation coefficient), information theory (mutual information), regression based approaches, probabilistic approaches (Bayesian networks, naive byes), artificial neural networks, and fuzzy logic. The fuzzy logic, along with its hybridization with other intelligent approach, is well studied in GRNI due to its several advantages. In this paper, we present a consolidated review on fuzzy logic and its hybrid approaches for GRNI developed during last two decades.Comment: 29 pages, 12 figures and 1 tabl