Biclustering has become a popular technique for the study of gene expression data, especially for discovering functionally related gene sets under different subsets of experimental conditions. Most of biclustering approaches use a measure or cost function that determines the quality of biclusters. In such cases, the development of both a suitable heuristics and a good measure for guiding the search are essential for discovering interesting biclusters in an expression matrix. Nevertheless, not all existing biclustering approaches base their search on evaluation measures for biclusters. There exists a diverse set of biclustering tools that follow different strategies and algorithmic concepts which guide the search towards meaningful results. In this paper we present a extensive survey of biclustering approaches, classifying them into two categories according to whether or not use evaluation metrics within the search method: biclustering algorithms based on evaluation measures and non metric-based biclustering algorithms. In both cases, they have been classified according to the type of meta-heuristics which they are based on.

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Some of the most influential factors in the quality of the solutions found by an evolutionary algorithm (EA) are a correct coding of the search space and an appropriate evaluation function of the potential solutions. EAs are often used to learn decision rules from datasets, which are encoded as individuals in the genetic population. In this paper, the coding of the search space for the obtaining of those decision rules is approached, i.e., the representation of the individuals of the genetic population and also the design of specific genetic operators. Our approach, called "natural coding," uses one gene per feature in the dataset (continuous or discrete). The examples from the datasets are also encoded into the search space, where the genetic population evolves, and therefore the evaluation process is improved substantially. Genetic operators for the natural coding are formally defined as algebraic expressions. Experiments with several datasets from the University of California at Irvine (UCI) machine learning repository show that as the genetic operators are better guided through the search space, the number of rules decreases considerably while maintaining the accuracy, similar to that of hybrid coding, which joins the well-known binary and real representations to encode discrete and continuous attributes, respectively. The computational cost associated with the natural coding is also reduced with regard to the hybrid representation. Our algorithm, HIDER*, has been statistically tested against C4.5 and C4.5 Rules, and performed well. The knowledge models obtained are simpler, with very few decision rules, and therefore easier to understand, which is an advantage in many domains. The experiments with high-dimensional datasets showed the same good behavior, maintaining the quality of the knowledge model with respect to prediction accuracy. Index Terms-Decision rules, evolutionary encoding, supervised learning. I. INTRODUCTION D ECISION RULES are especially relevant in problems related to supervised learning. Given a dataset with continuous and discrete features or attributes, and a class label, we try to find a rule set that describes the knowledge within data or classifies new unseen data. When the feature is discrete, the rules take the form of "if , then class," where the values are not necessarily all those that the feature can take. When the feature is continuous, typically the rules take the form of "if then class," where

BackgroundBiclustering algorithms for microarray data aim at discovering functionally related gene sets under different subsets of experimental conditions. Due to the problem complexity and the characteristics of microarray datasets, heuristic searches are usually used instead of exhaustive algorithms. Also, the comparison among different techniques is still a challenge. The obtained results vary in relevant features such as the number of genes or conditions, which makes it difficult to carry out a fair comparison. Moreover, existing approaches do not allow the user to specify any preferences on these properties.ResultsHere, we present the first biclustering algorithm in which it is possible to particularize several biclusters features in terms of different objectives. This can be done by tuning the specified features in the algorithm or also by incorporating new objectives into the search. Furthermore, our approach bases the bicluster evaluation in the use of expression patterns, being able to recognize both shifting and scaling patterns either simultaneously or not. Evolutionary computation has been chosen as the search strategy, naming thus our proposal Evo-Bexpa (Evolutionary Biclustering based in Expression Patterns).ConclusionsWe have conducted experiments on both synthetic and real datasets demonstrating Evo-Bexpa abilities to obtain meaningful biclusters. Synthetic experiments have been designed in order to compare Evo-Bexpa performance with other approaches when looking for perfect patterns. Experiments with four different real datasets also confirm the proper performing of our algorithm, whose results have been biologically validated through Gene Ontology.

Abstract-The increasing amount of information available is encouraging the search for efficient techniques to improve the data mining methods, especially those which consume great computational resources, such as evolutionary computation. Efficacy and efficiency are two critical aspects for knowledge-based techniques. The incorporation of knowledge into evolutionary algorithms (EAs) should provide either better solutions (efficacy) or the equivalent solutions in shorter time (efficiency), regarding the same evolutionary algorithm without incorporating such knowledge. In this paper, we categorize and summarize some of the incorporation of knowledge techniques for evolutionary algorithms and present a novel data structure, called efficient evaluation structure (EES), which helps the evolutionary algorithm to provide decision rules using less computational resources. The EES-based EA is tested and compared to another EA system and the experimental results show the quality of our approach, reducing the computational cost about 50%, maintaining the global accuracy of the final set of decision rules.