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RNA represents a pivotal component of host–pathogen interactions. Human cytomegalovirus (HCMV) infection causes extensive alteration in host RNA metabolism, but the functional relationship between the virus and cellular RNA processing remains largely unknown. Through loss-of-function screening, we show that HCMV requires multiple RNA-processing machineries for efficient viral lytic production. In particular, the cellular RNA-binding protein Roquin, whose expression is actively stimulated by HCMV, plays an essential role in inhibiting the innate immune response. Transcriptome profiling revealed Roquin-dependent global down-regulation of proinflammatory cytokines and antiviral genes in HCMV-infected cells. Furthermore, using cross-linking immunoprecipitation (CLIP)-sequencing (seq), we identified IFN regulatory factor 1 ( IRF1 ), a master transcriptional activator of immune responses, as a Roquin target gene. Roquin reduces IRF1 expression by directly binding to its mRNA, thereby enabling suppression of a variety of antiviral genes. This study demonstrates how HCMV exploits host RNA-binding protein to prevent a cellular antiviral response and offers mechanistic insight into the potential development of CMV therapeutics.

Following publication of the original article [1], the authors reported the following errors.

Gene regulatory networks (GRNs) describe regulatory relationships between transcription factors (TFs) and their target genes. Computational methods to infer GRNs typically combine evidence across different conditions to infer context-agnostic networks. We develop a method, Network Reprogramming using EXpression (NetREX), that constructs a context-specific GRN given context-specific expression data and a context-agnostic prior network. NetREX remodels the prior network to obtain the topology that provides the best explanation for expression data. Because NetREX utilizes prior network topology, we also develop PriorBoost, a method that evaluates a prior network in terms of its consistency with the expression data. We validate NetREX and PriorBoost using the “gold standard” E. coli GRN from the DREAM5 network inference challenge and apply them to construct sex-specific Drosophila GRNs. NetREX constructed sex-specific Drosophila GRNs that, on all applied measures, outperform networks obtained from other methods indicating that NetREX is an important milestone toward building more accurate GRNs., For many applications knowledge of context-specific gene regulatory networks (GRNs) is desirable, but their inference remains a challenge. Here, the authors introduce a method for construction of context-specific GRNs, and apply it to construct sex-specific Drosophila GRNs.

Deletions, commonly referred to as deficiencies by Drosophila geneticists, are valuable tools for mapping genes and for genetic pathway discovery via dose-dependent suppressor and enhancer screens. More recently, it has become clear that deviations from normal gene dosage are associated with multiple disorders in a range of species including humans. While we are beginning to understand some of the transcriptional effects brought about by gene dosage changes and the chromosome rearrangement breakpoints associated with them, much of this work relies on isolated examples. We have systematically examined deficiencies of the left arm of chromosome 2 and characterize gene-by-gene dosage responses that vary from collapsed expression through modest partial dosage compensation to full or even over compensation. We found negligible long-range effects of creating novel chromosome domains at deletion breakpoints, suggesting that cases of gene regulation due to altered nuclear architecture are rare. These rare cases include trans de-repression when deficiencies delete chromatin characterized as repressive in other studies. Generally, effects of breakpoints on expression are promoter proximal (~100bp) or in the gene body. Effects of deficiencies genome-wide are in genes with regulatory relationships to genes within the deleted segments, highlighting the subtle expression network defects in these sensitized genetic backgrounds., Author Summary Deletions alter gene dose in heterozygotes and bring distant regions of the genome into juxtaposition. We find that the transcriptional dose response is generally varied, gene-specific and coherently propagates into gene expression regulatory networks. Analysis of expression profiles of deletion heterozygotes indicates that distinct genetic pathways are weakened in adult flies bearing different deletions, even-though they show minimal or no overt phenotypes. While there are exceptions, breakpoints have a minimal effect on gene expression of flanking genes, despite the fact that different regions of the genome are brought into contact and that important elements such as insulators are deleted. These data suggest that there is little effect of nuclear architecture and long-range enhancer and/or silencer promoter contact on gene expression in the compact Drosophila genome.

The autoimmune disorder Aicardi-Goutières syndrome (AGS) is characterized by a constitutive type I interferon response. SAMHD1 possesses both dNTPase and RNase activities and mutations in SAMHD1 cause AGS; however, how SAMHD1-deficiency causes the type I interferon response in patients with AGS remains unknown. Here, we show that endogenous RNA substrates accumulated in the absence of SAMHD1 act as a major immunogenic source for the type I interferon response. Reconstitution of SAMHD1-negative human cells with wild-type but not RNase-defective SAMHD1 abolishes spontaneous type I interferon induction. We further identify that the PI3K/AKT/IRF3 signaling pathway is essential for the type I interferon response in SAMHD1-deficient human monocytic cells. Treatment of PI3K or AKT inhibitors dramatically reduces the type I interferon signatures in SAMHD1-deficient cells. Moreover, SAMHD1/AKT1 double knockout relieves the type I interferon signatures to the levels observed for wild-type cells. Identification of AGS-related RNA sensing pathway provides critical insights into the molecular pathogenesis of the type I interferonopathies such as AGS and overlapping autoimmune disorders.

Understanding gene regulation is a fundamental step towards understanding of how cells function and respond to environmental cues and perturbations. An important step in this direction is to infer the transcription factor-gene regulatory network (GRN). However gene regulatory networks are typically constructed disregarding the fact that regulatory programs are conditioned on tissue type, developmental stage, sex, and other factors. Collecting multitude of features required for a reliable construction of GRNs such as physical features and functional features for every context of interest is costly. Therefore we need methods that is able to use the knowledge of a context-agnostic network for construction of a context specific regulatory network. To address this challenge we developed a computational approach that uses context specific expression data and a GRN constructed in a different but related context to construct a context specific GRN. Our method, NetREX, is inspired by network component analysis that estimates TF activities and their influences on target genes given predetermined topology of a TF-gene network. To predict a network under a different condition, NetREX removes the restriction that the topology of the TF-gene network is fixed and allows for adding and removing edges to that network. To solve the corresponding optimization problem, we provide a general mathematical framework allowing use of the recently proposed PALM technique and develop a convergent algorithm. We tested our NetREX on simulated data and subsequently applied it to gene expression data in adult females from Drosophila deletion (DrosDel) panel. The networks predicted by NetREX showed higher biological consistency than alternative approaches. In addition, we used the list of recently identified targets of the Doublesex (DSX) transcription factor to demonstrate the predictive power of our method., RECOMB 2017

Specific cellular fates and functions depend on differential gene expression, which occurs primarily at the transcriptional level, controlled by complex regulatory networks of transcription factors. Transcription factors act through combinatorial interactions with other transcription factors, co-factors and chromatin-remodelling proteins. We present a study of 459 Drosophila melanogaster transcription related factors, defining protein-protein interactions using a co-affinity purification mass spectrometry methodology, representing approximately half of the established catalogue of transcription factors. We probe this network in vivo, demonstrating functional interactions for many interacting proteins testing the predictive value for our data set. Building on these analyses, we combine regulatory network inference models with physical interactions to define an integrated network, connecting combinatorial transcription factor protein interactions to the transcriptional regulatory network of the cell. We use this integrated network as a tool to connect the functional network of genetic modifiers related to mastermind, a transcriptional co-factor of the Notch pathway.

One of the obstacles hindering a better understanding of cancer is its heterogeneity. However, computational approaches to model cancer heterogeneity have lagged behind. To bridge this gap, we have developed a new probabilistic approach that models individual cancer cases as mixtures of subtypes. Our approach can be seen as a meta-model that summarizes the results of a large number of alternative models. It does not assume predefined subtypes nor does it assume that such subtypes have to be sharply defined. Instead given a measure of phenotypic similarity between patients and a list of potential explanatory features, such as mutations, copy number variation, microRNA levels, etc., it explains phenotypic similarities with the help of these features. We applied our approach to Glioblastoma Multiforme (GBM). The resulting model Prob_GBM, not only correctly inferred known relationships but also identified new properties underlining phenotypic similarities. The proposed probabilistic framework can be applied to model relations between similarity of gene expression and a broad spectrum of potential genetic causes.

Author Summary Cancer is now increasingly studied from the perspective of dysregulated pathways, rather than as a disease resulting from mutations of individual genes. A pathway-centric view acknowledges the heterogeneity between genomic profiles from different cancer patients while assuming that the mutated genes are likely to belong to the same pathway and cause similar disease phenotypes. Indeed, network-centric approaches have proven to be helpful for finding genotypic causes of diseases, classifying disease subtypes, and identifying drug targets. In this review, we discuss how networks can be used to help understand patient-to-patient variations and how one can leverage this variability to elucidate interactions between cancer drivers.

Gene copy number variations are associated with many disorders characterized by high phenotypic heterogeneity. Disease penetrance differs even in genetically identical twins. Can such heterogeneity arise, in part, from increased expression variability of one dose genes? While increased variability in the context of single cell gene expression is well recognized, our computational simulations indicated that in a multicellular organism intrinsic single cell level noise should cancel out and thus the impact of gene copy reduction on organismal level expression variability must be due to something else. To systematically examine the impact of gene dose reduction on expression variability in a multi-cellular organism, we performed experimental gene expression measurements in Drosophila DrosDel autosomal deficiency lines. Genome-wide analysis revealed that autosomal one dose genes have higher gene expression variability relative to two dose genes. In flies, gene dose reduction is often accompanied by dosage compensation at the gene expression level. Surprisingly, expression noise was increased by compensation. This increased compensation-dependent variability was found to be a property of one dose autosomal genes but not X-liked genes in males despite the fact that they too are dosage compensated, suggesting that sex chromosome dosage compensation also results in noise reduction. Previous studies attributed autosomal dosage compensation to feedback loops in interaction networks. Our results suggest that these feedback loops are not optimized to deliver consistent responses to gene deletion events and thus gene deletions can lead to heterogeneous responses even in the context of an identical genetic background. Additionally, we show that expression variation associated with reduced dose of transcription factors propagate through the gene interaction network, impacting a large number of downstream genes. These properties of gene deletions could contribute to the phenotypic heterogeneity of diseases associated with haploinsufficiency.

Deletions, commonly referred to as deficiencies by Drosophila geneticists, are valuable tools for mapping genes and for genetic pathway discovery via dose-dependent suppressor and enhancer screens. More recently, it has become clear that deviations from normal gene dosage are associated with multiple disorders in a range of species including humans. While we are beginning to understand some of the transcriptional effects brought about by gene dosage changes and the chromosome rearrangement breakpoints associated with them, much of this work relies on isolated examples. We have systematically examined deficiencies on the left arm of chromosome 2 and characterize gene-by-gene dosage responses that vary from collapsed expression through modest partial dosage compensation to full or even over compensation. We found negligible long-range effects of creating novel chromosome domains at deletion breakpoints, suggesting that cases of changes in gene regulation due to altered nuclear architecture are rare. These rare cases include trans de-repression when deficiencies delete chromatin characterized as repressive in other studies. Generally, effects of breakpoints on expression are promoter proximal (~100 bp) or within the gene body. Genome-wide effects of deficiencies are observed at genes with regulatory relationships to genes within the deleted segments, highlighting the subtle expression network defects in these sensitized genetic backgrounds.

Motivation: The data gathered by the Pan-Cancer initiative has created an unprecedented opportunity for illuminating common features across different cancer types. However, separating tissue-specific features from across cancer signatures has proven to be challenging. One of the often-observed properties of the mutational landscape of cancer is the mutual exclusivity of cancer driving mutations. Even though studies based on individual cancer types suggested that mutually exclusive pairs often share the same functional pathway, the relationship between across cancer mutual exclusivity and functional connectivity has not been previously investigated. Results: We introduce a classification of mutual exclusivity into three basic classes: within tissue type exclusivity, across tissue type exclusivity and between tissue type exclusivity. We then combined across-cancer mutual exclusivity with interactions data to uncover pan-cancer dysregulated pathways. Our new method, Mutual Exclusivity Module Cover (MEMCover) not only identified previously known Pan-Cancer dysregulated subnetworks but also novel subnetworks whose across cancer role has not been appreciated well before. In addition, we demonstrate the existence of mutual exclusivity hubs, putatively corresponding to cancer drivers with strong growth advantages. Finally, we show that while mutually exclusive pairs within or across cancer types are predominantly functionally interacting, the pairs in between cancer mutual exclusivity class are more often disconnected in functional networks. Contact: przytyck@ncbi.nlm.nih.gov Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Most previous approaches to model biochemical networks have focused either on the characterization of a network structure with a number of components or on the estimation of kinetic parameters of a network with a relatively small number of components. For system-level understanding, however, we should examine both the interactions among the components and the dynamic behaviors of the components. A key obstacle to this simultaneous identification of the structure and parameters is the lack of data compared with the relatively large number of parameters to be estimated. Hence, there are many plausible networks for the given data, but most of them are not likely to exist in the real system. Results: We propose a new representation named S-trees for both the structural and dynamical modeling of a biochemical network within a unified scheme. We further present S-tree based genetic programming to identify the structure of a biochemical network and to estimate the corresponding parameter values at the same time. While other evolutionary algorithms require additional techniques for sparse structure identification, our approach can automatically assemble the sparse primitives of a biochemical network in an efficient way. We evaluate our algorithm on the dynamic profiles of an artificial genetic network. In 20 trials for four settings, we obtain the true structure and their relative squared errors are Availability: The executable program and data are available from the authors upon request. Contact: ckh-sb@snu.ac.kr or btzhang@snu.ac.kr

Summary Recent genomic analyses of pathologically defined tumor types identify "within-a-tissue" disease subtypes. However, the extent to which genomic signatures are shared across tissues is still unclear. We performed an integrative analysis using five genome-wide platforms and one proteomic platform on 3,527 specimens from 12 cancer types, revealing a unified classification into 11 major subtypes. Five subtypes were nearly identical to their tissue-of-origin counterparts, but several distinct cancer types were found to converge into common subtypes. Lung squamous, head and neck, and a subset of bladder cancers coalesced into one subtype typified by TP53 alterations, TP63 amplifications, and high expression of immune and proliferation pathway genes. Of note, bladder cancers split into three pan-cancer subtypes. The multiplatform classification, while correlated with tissue-of-origin, provides independent information for predicting clinical outcomes. All data sets are available for data-mining from a unified resource to support further biological discoveries and insights into novel therapeutic strategies.

Background Structural rearrangements of the genome resulting in genic imbalance due to copy number change are often deleterious at the organismal level, but are common in immortalized cell lines and tumors, where they may be an advantage to cells. In order to explore the biological consequences of copy number changes in the Drosophila genome, we resequenced the genomes of 19 tissue-culture cell lines and generated RNA-Seq profiles. Results Our work revealed dramatic duplications and deletions in all cell lines. We found three lines of evidence indicating that copy number changes were due to selection during tissue culture. First, we found that copy numbers correlated to maintain stoichiometric balance in protein complexes and biochemical pathways, consistent with the gene balance hypothesis. Second, while most copy number changes were cell line-specific, we identified some copy number changes shared by many of the independent cell lines. These included dramatic recurrence of increased copy number of the PDGF/VEGF receptor, which is also over-expressed in many cancer cells, and of bantam, an anti-apoptosis miRNA. Third, even when copy number changes seemed distinct between lines, there was strong evidence that they supported a common phenotypic outcome. For example, we found that proto-oncogenes were over-represented in one cell line (S2-DRSC), whereas tumor suppressor genes were under-represented in another (Kc167). Conclusion Our study illustrates how genome structure changes may contribute to selection of cell lines in vitro. This has implications for other cell-level natural selection progressions, including tumorigenesis. Electronic supplementary material The online version of this article (doi:10.1186/gb-2014-15-8-r70) contains supplementary material, which is available to authorized users.

System identification involves determination of the functional structure of a target system that underlies the observed data. In this paper, we present a probabilistic evolutionary method that optimizes system architectures for the identification of unknown target systems. The method is distinguished from existing evolutionary algorithms (EAs) in that the individuals are generated from a probability distribution as in Markov chain Monte Carlo (MCMC). It is also distinguished from conventional MCMC methods in that the search is population-based as in standard evolutionary algorithms. The effectiveness of this hybrid of evolutionary computation and MCMC is tested on a practical problem, i.e., evolving neural net architectures for the identification of nonlinear dynamic systems. Experimental evidence supports that evolutionary MCMC (or eMCMC) exploits the efficiency of simple evolutionary algorithms while maintaining the robustness of MCMC methods and outperforms either approach used alone.

Genetic programming provides a useful tool for emergent computation and artificial life research. However, conventional genetic programming is not efficient enough to solve realistic multiagent tasks consisting of several emergent behaviors that need to be coordinated in the proper sequence. In this paper, we describe a novel method, called fitness switching, for evolving composite cooperative behaviors in multiple robotic agents using genetic programming. The method maintains a pool of basis fitness functions which are switched from simpler ones to more complex ones. The performance is demonstrated and evaluated in the context of a table transport problem. Experimental results show that the fitness switching method is an effective mechanism for evolving collective behaviors which can not be solved by simple genetic programming.

The Cancer Genome Atlas (TCGA) Research Network has profiled and analyzed large numbers of human tumors to discover molecular aberrations at the DNA, RNA, protein and epigenetic levels. The resulting rich data provide a major opportunity to develop an integrated picture of commonalities, differences and emergent themes across tumor lineages. The Pan-Cancer initiative compares the first 12 tumor types profiled by TCGA. Analysis of the molecular aberrations and their functional roles across tumor types will teach us how to extend therapies effective in one cancer type to others with a similar genomic profile., National Cancer Institute (U.S.), National Human Genome Research Institute (U.S.)

Society increasingly depends on large distributed systems, such as the Internet and Web-based service-oriented architectures deployed over the Internet. Such systems constantly evolve as new software components are injected to provide increased functionality, better performance and enhanced security. Unfortunately, designers lack effective methods to predict how new components might influence macroscopic behavior. Lacking effective methods, designers rely on engineering techniques, such as: analysis of critical algorithms at small scale and under limiting assumptions; factor-at-a-time simulations conducted at modest scale; and empirical measurements in small test beds. Such engineering techniques enable designers to characterize selected properties of new components but reveal little about likely dynamics at global scale. In this paper, we outline an approach that can be used to predict macroscopic dynamics when new components are deployed in a large distributed system. Our approach combines two main methods: scale reduction and multidimensional data analysis techniques. Combining these methods, we can search a wide parameter space to identify factors likely to drive global system response and we can predict the resulting macroscopic dynamics of key system behaviors. We demonstrate our approach in the context of the Internet, where researchers, motivated by a desire to increase user performance, have proposed new algorithms to replace the standard congestion control mechanism. Previously, the proposed algorithms were studied in three ways: using analytical models of single data flows, using empirical measurements in test beds where a few data flows compete for bandwidth, and using simulations at modest scale with a few sequentially varied parameters. In contrast, by applying our approach, we simulated configurations covering four-tier network topologies, spanning continental and global distances, comprising routers operating at state-of-the-art speeds and transporting more than 105 simultaneous data flows with varying traffic patterns and temporary spatiotemporal congestion. Our findings identify the main factors influencing macroscopic dynamics of Internet congestion control, and define the specific combination of factors that must hold for users to realize improved performance. We also uncover potential for one proposed algorithm to cause widespread performance degradation. Previous engineering studies of the proposed congestion control algorithms were unable to reveal such essential information.Copyright © 2011 by ASME

High-throughput data such as microarrays make it possible to investigate the molecular-level mechanism of cancer more efficiently. Computational methods boost the microarray analysis by managing large and complex data systematically. However, combinatorial interactions among genes have not been considered as a unit of the analysis since previous methods mainly focus on a whole gene or a single isolated gene. Here, we introduce a molecular evolutionary algorithm called probabilistic library model (PLM). In the PLM, library elements are generated from gene combinations. An evolutionary procedure is adopted to learn the probabilistic distribution of training samples. We apply the PLM to prostate cancer microarray data. The experimental results show that the PLM classifiers perform better than conventional methods such as neural networks and decision trees in accuracy. We also examine the evolved library to find cancer-related gene combinations.

With the availability of the time series data from the high-throughput technologies, diverse approaches have been proposed to model gene regulatory networks. Compared with others, S-system has the advantage for these tasks in the sense that it can provide both quantitative (structural) and qualitative (dynamical) modeling in one framework. However, it is not easy to identify the structure of the true network since the number of parameters to be estimated is much larger than that of the available data. Moreover, conventional parameter estimation requires the time-consuming numerical integration to reproduce dynamic profiles for the S-system. In this paper, we propose multi-stage evolutionary algorithms to identify gene regulatory networks efficiently. With the symbolic regression by genetic programming (GP), we can evade the numerical integration steps. This is because the estimation of slopes for each time-course data can be obtained from the results of GP. We also develop hybrid evolutionary algorithms and modified fitness evaluation function to identify the structure of gene regulatory networks and to estimate the corresponding parameters at the same time. By applying the proposed method to the identification of an artificial genetic network, we verify its capability of finding the true S-system.

In evolutionary continuous optimization by building and using probabilistic models, the multivariate Gaussian distribution and their variants or extensions such as the mixture of Gaussians have been used popularly. However, this Gaussian assumption is often violated in many real problems. In this paper, we propose a new continuous estimation of distribution algorithms (EDAs) with the variational Bayesian independent component analyzers mixture model (vbICA-MM) for allowing any distribution to be modeled. We examine how this sophisticated density estimation technique has influence on the performance of the optimization by employing the same selection and population alternation schemes used in the previous EDAs. Our experimental results support that the presented EDAs achieve better performance than previous EDAs with ICA and Gaussian mixture- or kernel-based approaches.

Evolutionary optimization algorithms based on the probability models have been studied to capture the relationship between variables in the given problems and finally to find the optimal solutions more efficiently. However, premature convergence to local optima still happens in these algorithms. Many researchers have used the multiple populations to prevent this ill behavior since the key point is to ensure the diversity of the population. In this paper, we propose a new estimation of distribution algorithm by using the mixture of factor analyzers (MFA) which can cluster similar individuals in a group and explain the high order interactions with the latent variables for each group concurrently. We also adopt a stochastic selection method based on the evolutionary Markov chain Monte Carlo (eMCMC). Our experimental results support that the presented estimation of distribution algorithms with MFA and eMCMC-like selection scheme can achieve better performance for continuous optimization problems.

Many evolutionary algorithms have been studied to build and use a probability distribution model of the population for optimization problems. Most of these methods tried to represent explicitly the relationship between variables in the problem with factorization techniques or a graphical model such as Bayesian or Gaussian networks. Thus enormous computational cost is required for constructing those models when the problem size is large. We propose a new estimation of distribution algorithm by using probabilistic principal component analysis (PPCA) which can explain the high order interactions with the latent variables. Since there are no explicit search procedures for the probability density structure, it is possible to rapidly estimate the distribution and readily sample the new individuals from it. Our experimental results support that the presented estimation of distribution algorithms with PPCA can find good solutions more efficiently than other EDAs for the continuous spaces.

Bayesian evolutionary algorithms (BEAs) are a probabilistic model for evolutionary computation. Instead of simply generating new populations as in conventional evolutionary algorithms, the BEAs attempt to explicitly estimate the posterior distribution of the individuals from their prior probability and likelihood, and then sample offspring from the distribution. We apply the Bayesian evolutionary algorithms to evolving neural trees, i.e. tree-structured neural networks. Explicit formulae for specifying the distributions on the model space are provided in the context of neural trees. The effectiveness and robustness of the method is demonstrated on the time series prediction problem. We also study the effect of the population size and the amount of information exchanged by subtree crossover and subtree mutations. Experimental results show that small-step mutation-oriented variations are most effective when the population size is small, while large-step recombinative variations are more effective for large population sizes.

Classification of patient samples is a crucial aspect of cancer diagnosis. DNA hybridization arrays simultaneously measure the expression levels of thousands of genes and it has been suggested that gene expression may provide the additional information needed to improve cancer classification and diagnosis. This paper presents methods for analyzing gene expression data to classify cancer types. Machine learning techniques, such as Bayesian networks, neural trees, and radial basis function (RBF) networks, are used for the analysis of the CAMDA Data Set 2. These techniques have their own properties including the ability of finding important genes for cancer classification, revealing relationships among genes, and classifying cancer. This paper reports on comparative evaluation of the experimental results of these methods.

Genetic programming evolves Lisp-like programs rather than fixed size linear strings. This representational power combined with generality makes genetic programming an interesting tool for automatic programming and machine learning. One weakness is the enormous time required for evolving complex programs. In this paper we present a method for accelerating evolution speed of genetic programming by active selection of fitness cases during the run. In contrast to conventional genetic programming in which all the given training data are used repeatedly, the presented method evolves programs using only a subset of given data chosen incrementally at each generation. This method is applied to the evolution of collective behaviors for multiple robotic agents. Experimental evidence supports that evolving programs on an incrementally selected subset of fitness cases can significantly reduce the fitness evaluation time without sacrificing generalization accuracy of the evolved programs.