Heuristic Selection Research Articles

SA-MSVM: Hybrid Heuristic Algorithm-based Feature Selection for Sentiment Analysis in Twitter

One of the drastically growing and emerging research areas used in most information technology industries is Bigdata analytics. Bigdata is created from social websites like Facebook, WhatsApp, Twitter, etc. Opinions about products, persons, initiatives, political issues, research achievements, and entertainment are discussed on social websites. The unique data analytics method cannot be applied to various social websites since the data formats are different. Several approaches, techniques, and tools have been used for big data analytics, opinion mining, or sentiment analysis, but the accuracy is yet to be improved. The proposed work is motivated to do sentiment analysis on Twitter data for cloth products using Simulated Annealing incorporated with the Multiclass Support Vector Machine (SA-MSVM) approach. SA-MSVM is a hybrid heuristic approach for selecting and classifying text-based sentimental words following the Natural Language Processing (NLP) process applied on tweets extracted from the Twitter dataset. A simulated annealing algorithm searches for relevant features and selects and identifies sentimental terms that customers criticize. SA-MSVM is implemented, experimented with MATLAB, and the results are verified. The results concluded that SA-MSVM has more potential in sentiment analysis and classification than the existing Support Vector Machine (SVM) approach. SA-MSVM has obtained 96.34% accuracy in classifying the product review compared with the existing systems.

Heuristic actuator selection with the use of data of nonlinear optimal control for fluid flows

In this paper, we propose a heuristic method to select actuators for optimal control based on the Navier–Stokes equations. In the proposed method, the optimal control with the use of all candidates of actuators is applied to a fluid flow in a numerical simulation, and the optimal control inputs for all the candidates are acquired. Actuators are then selected in the descending order of the norms of these optimal control inputs. This procedure is numerically tractable for some of two-dimensional and three-dimensional fluid flows. The proposed method is demonstrated for an actuator selection problem for a two-dimensional flow around a circular cylinder at Reynolds number 100. Numerical simulations reveal that actuators are selected so that the optimal controller can take the state close to the steady state with a small control effort.

A lightweight path consistency verification based on INT in SDN.

The existing path consistency verification solutions in software-defined networking (SDN) were implemented by proactive injecting large number of probing packets or by embedding linear-scale tags as the path lengthens, which incurred significant bandwidth and communication overhead. A lightweight path consistency validation mechanism based on in-band network telemetry (INT) in SDN is proposed. Based on INT, in the scheme, the ingress switch inserts a telemetry instruction header with probability, each subsequent switch updates the telemetry data using a uniform sampling algorithm and only carries partial path information in INT packet to keep the head space size constant, the egress switch reports the final sampled telemetry data to the controller to verify the path compliance according to aggregated telemetry data. A heuristic flow selection algorithm is proposed to implement network-level path consistency validation. The proposed scheme was implemented and evaluated. The analyses and experiments demonstrate the proposed mechanism effectively limits the packet head overhead and introduces less than 7% of additional forwarding delays and 6% of throughput degradation at most.

Hyper-heuristics with Path Relinking applied to the Generalised Time-Dependent ATSP in air travel

In this work we propose the use of path relinking within a hyper-heuristic framework to solve the Generalised Time-Dependent Asymmetric Traveling Salesman problem applied to Air Travel. We implemented several heuristic selection methods, such as Simple Random, Random Descent, Random Permutation and Reinforcement Learning. We were able to achieve very good solutions for 13 of the 14 instances from a well-known benchmark set, evidencing that the adequate use of a hyper-heuristic framework with path relinking can be very efficient in improving the final results.

The use of information technologies for the research and modernization of the separation of grain raw materials

The article presents algorithms for the operation of an information and measurement system describing the technology of grinding wheat grain. The properties of grain processing products are determined by the influence of natural and climatic factors, and in addition by the method of grinding. The spread of information technologies has made it possible to improve the existing algorithms of information and measurement systems for wheat processing, as well as to form new processing methods due to a more accurate assessment of grain properties. Various wheat varieties were studied, trial grinds of which were produced at a laboratory mill. The increase in the output of industry enterprises is ensured by heuristic selection of the most accurate operating mode of separators of various types. Empirical models serve the purpose of predicting the quality indicators of grain mass after its separation. Digitalization of the grain industry has made it possible to improve the accuracy of granulometric analysis in the field of grain particle classification by shape and size. Artificial neural networks and technical vision algorithms as part of an intelligent system were used to process the appearance of grain after grinding. Appropriate software has been developed for this purpose. To divide wheat into classes, it is proposed to use grain hardness indicators. A systematic approach was used to detect and distribute grain particles after grinding according to geometric characteristics. A brief description of the algorithm of operation of such a system consists in finding the contours of crushed grain particles and measuring their size and shape. The accuracy of using such an intelligent system when measuring grain hardness in comparison with traditional methods, as experiments have shown, is less than 3.5%. The introduction of such intelligent systems at industrial facilities of the food industry will improve the quality of the enterprise and the output of finished products.

An Investigation of Stochastic Variance Reduction Algorithms for Relative Difference Penalized 3D PET Image Reconstruction.

Penalised PET image reconstruction algorithms are often accelerated during early iterations with the use of subsets. However, these methods may exhibit limit cycle behaviour at later iterations due to variations between subsets. Desirable converged images can be achieved for a subclass of these algorithms via the implementation of a relaxed step size sequence, but the heuristic selection of parameters will impact the quality of the image sequence and algorithm convergence rates. In this work, we demonstrate the adaption and application of a class of stochastic variance reduction gradient algorithms for PET image reconstruction using the relative difference penalty and numerically compare convergence performance to BSREM. The two investigated algorithms are: SAGA and SVRG. These algorithms require the retention in memory of recently computed subset gradients, which are utilised in subsequent updates. We present several numerical studies based on Monte Carlo simulated data and a patient data set for fully 3D PET acquisitions. The impact of the number of subsets, different preconditioners and step size methods on the convergence of regions of interest values within the reconstructed images is explored. We observe that when using constant preconditioning, SAGA and SVRG demonstrate reduced variations in voxel values between subsequent updates and are less reliant on step size hyper-parameter selection than BSREM reconstructions. Furthermore, SAGA and SVRG can converge significantly faster to the penalised maximum likelihood solution than BSREM, particularly in low count data.

Wireless edge device intelligent task offloading in mobile edge computing using hyper-heuristics

To overcome with the computation limitation of resource-constrained wireless IoT edge devices, providing an efficient task computation offloading and resource allocation in distributed mobile edge computing environment is consider as a challenging and promising solution. Hyper-heuristic in recent times is gaining popularity due to its general applicability of same solution to solve different types of problems. Hyper-heuristic is generally a heuristic method or framework which iteratively evaluates and chooses the best low-level heuristic, to solve different types of problems. In this paper, we try to solve wireless device task offloading in mobile edge computing, which is a non-convex and NP-Hard problem by using a proposed novel Hyper-Heuristic Framework using Stochastic Heuristic Selection (HHFSHS) using Contextual Multi-Armed Bandit (CMAB) with Epsilon-Decreasing strategy, considering two key Quality of Service (QoS) objectives computation time and energy consumption. These multiobjective criteria are modeled as single-objective optimization problem with the goal to minimize latency and energy consumption of wireless devices without losing the pareto optimality. Finally, evaluate its performance by comparing with other individual meta-heuristic algorithms.

Leveraging heuristic client selection for enhanced secure federated submodel learning

As the number of clients for federated learning (FL) has expanded to the billion level, a new research branch named secure federated submodel learning (SFSL) has emerged. In SFSL, mobile clients only download a tiny ratio of the global model from the coordinator’s global. However, SFSL provides little guarantees on the convergence and accuracy performance as the covered items may be highly biased. In this work, we formulate the problem of client selection through optimizing unbiased coverage of item index set for enhancing SFSL performance. We analyze the NP-hardness of this problem and propose a novel heuristic multi-group client selection framework by jointly optimizing index diversity and similarity. Specifically, heuristic exploration on some random client groups are performed progressively for an empirical approximate solution. Meanwhile, private set operations are used to preserve the privacy of participated clients. We implement the proposal by simulating large-scale SFSL application in a lab environment and conduct evaluations on two real-world data-sets. The results demonstrate the performance (w.r.t., accuracy and convergence speed) superiority of our selection algorithm than SFSL. The proposal is also shown to yield significant computation advantage with similar communication performance as SFSL.

Conflict-Free Joint Sampling for Preference Satisfaction through Quantum Interference

Collective decision-making is vital for recent information and communications technologies. In our previous research, we mathematically derived conflict-free joint decision-making that optimally satisfies players' probabilistic preference profiles. However, two problems exist regarding the optimal joint decision-making method. First, as the number of choices increases, the computational cost of calculating the optimal joint selection probability matrix explodes. Second, to derive the optimal joint selection probability matrix, all players must disclose their probabilistic preferences. Now, it is noteworthy that explicit calculation of the joint probability distribution is not necessarily needed; what is necessary for collective decisions is sampling. This study examines several sampling methods that converge to heuristic joint selection probability matrices that satisfy players' preferences. We show that they can significantly reduce the above problems of computational cost and confidentiality. We analyze the probability distribution each of the sampling methods converges to, as well as the computational cost required and the confidentiality secured. In particular, we introduce two conflict-free joint sampling methods through quantum interference of photons. The first system allows the players to hide their choices while satisfying the players' preferences almost perfectly when they have the same preferences. The second system, where the physical nature of light replaces the expensive computational cost, also conceals their choices under the assumption that they have a trusted third party. This paper has been published in Phys. Rev. Applied 18, 064018 (2022) (DOI: 10.1103/PhysRevApplied.18.064018).

Diffusion-weighted imaging-based radiomics in epithelial ovarian tumors: Assessment of histologic subtype.

Epithelial ovarian tumors (EOTs) are a group of heterogeneous neoplasms. It is importance to preoperatively differentiate the histologic subtypes of EOTs. Our study aims to investigate the potential of radiomics signatures based on diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps for categorizing EOTs. This retrospectively enrolled 146 EOTs patients [34 with borderline EOT(BEOT), 30 with type I and 82 with type II epithelial ovarian cancer (EOC)]. A total of 390 radiomics features were extracted from DWI and ADC maps. Subsequently, the LASSO algorithm was used to reduce the feature dimensions. A radiomics signature was established using multivariable logistic regression method with 3-fold cross-validation and repeated 50 times. Patients with bilateral lesions were included in the validation cohort and a heuristic selection method was established to select the tumor with maximum probability for final consideration. A nomogram incorporating the radiomics signature and clinical characteristics was also developed. Receiver operator characteristic, decision curve analysis (DCA), and net reclassification index (NRI) were applied to compare the diagnostic performance and clinical net benefit of predictive model. For distinguishing BEOT from EOC, the radiomics signature and nomogram showed more favorable discrimination than the clinical model (0.915 vs. 0.852 and 0.954 vs. 0.852, respectively) in the training cohort. In classifying early-stage type I and type II EOC, the radiomics signature exhibited superior diagnostic performance over the clinical model (AUC 0.905 vs. 0.735). The diagnostic efficacy of the nomogram was the same as that of the radiomics model with NRI value of -0.1591 (P = 0.7268). DCA also showed that the radiomics model and combined model had higher net benefits than the clinical model. Radiomics analysis based on DWI, and ADC maps serve as an effective quantitative approach to categorize EOTs.

An Hybrid Heuristic optimal relay selection strategy for energy efficient multi hop cooperative cellular communication

One of the major constraints for mobile relay networks is energy-efficient communication owing to the restricted battery power of user terminals. Among them, the production method is cooperative cellular networks that focus on enhancing the data rate, network reliability, and network system capacity and also improving the spectrum resources in the Fifth Generation (5 G) networks. Thus, recent research works investigate the selection of mobile relays in vehicular networks and also the relay selection in cooperative communication. Here, the mobile relay is selected by the base station (operator) for every mobile user for reducing the cost with improved data rates and reducing the total transmission power. The selection of relay is performed by considering the benefits or utilities offered to three players such as mobile relay, mobile user, and operator. Conventional relay node selection approaches face challenges regarding reduced network lifetime, delay, increased energy consumption, and collusion attacks. For next-generation wireless networks, both energy efficiency and capacity are vital. To cope with these problems, a new multi-objective derived energy efficient accurate selection of relay in multi-hop cooperative cellular communication in the vehicular network through a hybrid heuristic strategy is implemented. The conventional approaches are utilized for probability-based multi-hop broadcasting for low latency. On the other hand, the major demerits of cellular communication are developing an effective multi-hop communication strategy that generates hidden node problems, and also it decreases the speed of the vehicles and the lack of infrastructure in network topology. Thus, this paper aims to propose a hybrid optimization model to choose the lowest count of multi-hops among the source and destination for the cooperative cellular vehicular communication network. The major issue well thought-out here is accurately choosing the count of hops or relays for appropriate communication, which is carried out through the multi-objective function regarding link reliability, energy consumption, outage probability, mobility factor, and transmission delay of the system. The adoption of the new hybrid algorithm with the aid of the Best Fitness-derived Crow Tunicate Swarm Optimization (BF-CTSO) algorithm is designed as the major contribution for optimally selecting the multi-hops between source and destination in a cooperative cellular communication network. Numerical results show that the proposed energy-efficient cooperative communication system outperforms traditional methods.

An optimized adaptive ensemble model with feature selection for network intrusion detection

SummaryNetwork intrusion detection system (NIDS) is a key component to identify abnormal behavior of network systems and plays an important role in preventing the occurrence of network attacks. Although a considerable number of machine learning methods have been applied in the field of intrusion detection, it is still a challenge for existing solutions to achieve a good classification performance. The existing traffic datasets generally have redundant and irrelevant features, which hinder classifiers from making more accurate predictions. Furthermore, a single classifier has limited classification performance and may not be able to achieve a better detection performance overall in the face of unbalanced multi‐category traffic data. Therefore, in order to improve the classification performance of intrusion detection models, this paper proposes an adaptive ensemble model by combining feature selection techniques and effective ensemble methods. Firstly, a heuristic feature selection algorithm (NRS‐SSA) is proposed by introducing the neighborhood dependency degree of the neighborhood rough set (NRS) into the salp swarm algorithm (SSA). Then, an improved adaptive weighted voting algorithm is designed. The SSA is introduced to optimize the weight matrix when setting the voting weight. Finally, we use the designed voting algorithm to combine the classification advantages of homogeneous classifiers and heterogeneous classifiers, respectively, and propose an M‐Tree algorithm and an adaptive ensemble model. The experimental results on multiple intrusion detection datasets show that the proposed adaptive ensemble model achieves an advanced detection level.

Noise-resistant multilabel fuzzy neighborhood rough sets for feature subset selection

Feature selection attempts to capture the more discriminative features and plays a significant role in multilabel learning. As an efficient mathematical tool to handle incomplete and uncertain information, fuzzy rough set theory has been widely used in feature selection. However, there are two pivotal issues to be addressed when adopting fuzzy rough sets for multilabel data analysis. One is how to effectively characterize the discriminative ability of the features to multilabel samples. Another one is how to alleviate the effect of data noise on feature selection. For these reasons, this study mainly studies the noise-tolerant fuzzy neighborhood rough set model for multilabel learning and its feature selection strategy. Firstly, a parameterized hybrid fuzzy similarity relation is introduced to granulate multilabel data, and the parameterized fuzzy decision is extended to multilabel learning. Then, a noise-resistant multilabel fuzzy neighborhood rough set model using inclusion relation is constructed for describing the discriminative ability of the features to multilabel samples. Furthermore, a noise-resistant heuristic feature selection algorithm titled NRFSFN is designed. Besides, motivated by local sampling and ensemble strategy, an efficient extended version of NRFSFN is designed from the perspective of data distribution, named ENFSFN. Finally, a series of comparison experiments on sixteen multilabel datasets demonstrate the effectiveness of the proposed two algorithms.

A Distributed Task Scheduling Method Based on Conflict Prediction for Ad Hoc UAV Swarms

UAV swarms have attracted great attention, and are expected to be used in scenarios, such as search and rescue, that require many urgent jobs to be completed in a minimum time by multiple vehicles. For complex missions with tight constraints, careful assigning tasks is inseparable from the scheduling of these tasks, and multi-task distributed scheduling (MTDS) is required. The Performance Impact (PI) algorithm is an excellent solution for MTDS, but it suffers from the suboptimal solution caused by the heuristics for local task selection, and the deadlock problem that it may fall into an infinite cycle of exchanging the same task. In this paper, we improve the PI algorithm by integrating a new task-removal strategy and a conflict prediction mechanism into the task-removal phase and the task-inclusion phase, respectively. Specifically, the task-removal strategy results in better exploration of the inclusion of more tasks than the original PI by freeing up more space in the local scheduler, improving the suboptimal solution caused by the heuristics for local task selection, as done in PI. In addition, we design a conflict prediction mechanism that simulates adjacent vehicles performing inclusion operations as the criteria for local task inclusion. Therefore, it can reduce the deadlock ratio and iteration times of the MTDS algorithm. Furthermore, by combining the protocol stack with the physical transmission model, an ad-hoc network simulation platform is constructed, which is closer to the real-world network, and serves as the supporting environment for testing the MTDS algorithms. Based on the constructed ad-hoc network simulation platform, we demonstrate the advantage of the proposed algorithm over the original PI algorithm through Monte Carlo simulation of search and rescue tasks. The results show that the proposed algorithm can reduce the average time cost, increase the total allocation number under most random distributions of vehicles-tasks, and significantly reduce the deadlock ratio and the number of iteration rounds.

Feature selection based on double-hierarchical and multiplication-optimal fusion measurement in fuzzy neighborhood rough sets

In fuzzy neighborhood rough sets (FNRSs), uncertainty measurement performs mainly classification-hierarchical and multiplication-simple fusion, so the corresponding feature selection has advancement space. This paper aims to improve uncertainty measurement and feature selection via FNRSs. Two measurement strategies regarding class-hierarchical fusion and multiplication-optimal fusion are proposed, and three measure-based heuristic feature selection algorithms are developed. Concretely, fuzzy neighborhood self-information (FNSI) and joint entropy (FNJE) constitute two bases of heterogeneous fusion, and their multiplication fusion induces both the existing measure FNSIJE (which is based on classification-level fusion) and a new measure CFNSIJE (which is based on class-level fusion); furthermore, FNSIJE and CFNSIJE are extended to the optimal measures FNSIJEE and CFNSIJEE, respectively, by exponential parameterization. The four types of fusion measures acquire their calculation algorithms and granulation nonmonotonicity and systematically motivate four heuristic feature selection algorithms, i.e., the current FNSIJE-FS and the new CFNSIJE-FS, FNSIJEE-FS, and CFNSIJEE-FS. By using examples and experiments, relevant uncertainty measurement and granulation nonmonotonicity are validated, while the novel selection algorithms demonstrate better classification performances. This study establishes the hierarchical fusion and exponential expansion to acquire robust uncertainty measurement and optimal feature selection, and the measurement, nonmonotonicity, and selection have strong generalization for information fusion and rough-set learning.

Evolutionary Algorithm-Based Iterated Local Search Hyper-Heuristic for Combinatorial Optimization Problems

Hyper-heuristics are widely used for solving numerous complex computational search problems because of their intrinsic capability to generalize across problem domains. The fair-share iterated local search is one of the most successful hyper-heuristics for cross-domain search with outstanding performances on six problem domains. However, it has recorded low performances on three supplementary problems, namely knapsack, quadratic assignment, and maximum-cut problems, which undermines its credibility across problem domains. The purpose of this study was to design an evolutionary algorithm-based iterated local search (EA-ILS) hyper-heuristic that applies a novel mutation operator to control the selection of perturbative low-level heuristics in searching for optimal sequences for performance improvement. The algorithm was compared to existing ones in the hyper-heuristics flexible (HyFlex) framework to demonstrate its performance across the problem domains of knapsack, quadratic assignment, and maximum cut. The comparative results have shown that the EA-ILS hyper-heuristic can obtain the best median objective function values on 22 out of 30 instances in the HyFlex framework. Moreover, it has achieved superiority in its generalization capability when compared to the reported top-performing hyper-heuristic algorithms.

Beyond performance? The importance of subjective and objective physical appearance in award nominations and receptions in football

Despite an extensive body of research indicating multifaceted advantages for employees deemed physically attractive, factors that limit or even negate the attractiveness premium have not been sufficiently investigated. In this paper, we are interested in whether a rich set of physical appearance factors matter when performance information is transparently available, rather than – as is mostly the case in the labor force – imperfect or costly to obtain. To this end, we explore the labor market of professional football players in one of the world's most prestigious football leagues, namely the German Bundesliga. We investigate whether a rich set of physical appearance factors (including subjective attractiveness, facial symmetry, facial width-to-height ratio (fWHR), and body gestalt) affect players’ chances of being nominated or receiving the prestigious Player of the Month award. This study offers a unique opportunity to explore selection heuristics among team captains who are responsible for the nomination of the players, and the public who votes on the three top selections by captains. Our results indicate that individual performance tends to be a stronger driver of award success than physical appearance. Interestingly, performance can moderate some physical appearance factors (e.g., beauty matters more for lower performing players) and some positive assortative effects between captains and players along physical appearance are observed (captains with higher fWHR scores are more likely to nominate players who also have higher fWHR scores). In general, heuristics for captains are easier to identify empirically than for the public. We also find some differences; for example, only the public demonstrates a bias towards domestic players.

Feature selection based on self-information and entropy measures for incomplete neighborhood decision systems

For incomplete datasets with mixed numerical and symbolic features, feature selection based on neighborhood multi-granulation rough sets (NMRS) is developing rapidly. However, its evaluation function only considers the information contained in the lower approximation of the neighborhood decision, which easily leads to the loss of some information. To solve this problem, we construct a novel NMRS-based uncertain measure for feature selection, named neighborhood multi-granulation self-information-based pessimistic neighborhood multi-granulation tolerance joint entropy (PTSIJE), which can be used to incomplete neighborhood decision systems. First, from the algebra view, four kinds of neighborhood multi-granulation self-information measures of decision variables are proposed by using the upper and lower approximations of NMRS. We discuss the related properties, and find the fourth measure-lenient neighborhood multi-granulation self-information measure (NMSI) has better classification performance. Then, inspired by the algebra and information views simultaneously, a feature selection method based on PTSIJE is proposed. Finally, the Fisher score method is used to delete uncorrelated features to reduce the computational complexity for high-dimensional gene datasets, and a heuristic feature selection algorithm is raised to improve classification performance for mixed and incomplete datasets. Experimental results on 11 datasets show that our method selects fewer features and has higher classification accuracy than related methods.

Pointwise mutual information sparsely embedded feature selection

Feature selection is an effective approach to dimensionality reduction. Feature selection based on rough sets and fuzzy rough sets is extensively realized by introducing diversified heuristic information. Most existing heuristic feature selection methods ignore the fact that different features have distinct classification abilities when constructing feature evaluation functions. Due to the limitations of search strategies, locally optimal feature subsets are probably selected out. Embedded feature selection based on regression analysis can avoid falling into locally optimal feature subsets to a certain extent. However, only linear relationships between feature space and decision space have been involved. To overcome those problems, a pointwise mutual information sparsely embedded feature selection model (PMISEFS) is proposed in this paper. In this model, the pointwise fuzzy mutual information matrix is constructed based on fuzzy information granules to characterize the discernibility of features as well as the nonlinear relationship between the feature space and decision space for a data set. A classification information matrix is introduced to further describe the consistency between the predicted decision and real decisions of samples. The fuzzy mutual information tensor is embedded sparsely into the decision matrix and the sparsely embedded coefficients, called information fusion coefficients (IFCs) of features, are adaptively learnt by imposing a smooth constraint to avoid over-fitting. An embedded feature selection algorithm is designed to adaptively learn optimal IFCs. Extensive experiments on various benchmark data sets are conducted and experimental results demonstrate the superiority of the proposed model over the state-of-the-art heuristic as well as embedded feature selection methods.

Multitask Genetic Programming-Based Generative Hyperheuristics: A Case Study in Dynamic Scheduling.

Evolutionary multitask learning has achieved great success due to its ability to handle multiple tasks simultaneously. However, it is rarely used in the hyperheuristic domain, which aims at generating a heuristic for a class of problems rather than solving one specific problem. The existing multitask hyperheuristic studies only focus on heuristic selection, which is not applicable to heuristic generation. To fill the gap, we propose a novel multitask generative hyperheuristic approach based on genetic programming (GP) in this article. Specifically, we introduce the idea in evolutionary multitask learning to GP hyperheuristics with a suitable evolutionary framework and individual selection pressure. In addition, an origin-based offspring reservation strategy is developed to maintain the quality of individuals for each task. To verify the effectiveness of the proposed approach, comprehensive empirical studies have been conducted on the homogeneous and heterogeneous multitask dynamic flexible job shop scheduling. The results show that the proposed algorithm can significantly improve the quality of scheduling heuristics for each task in all the examined scenarios. In addition, the evolved scheduling heuristics verify the mutual help among the tasks in a multitask scenario.