Multi-objective Evolutionary Search Research Articles

Multi-objective evolutionary neural architecture search for network intrusion detection

Network Intrusion Detection (NID) becomes significantly important for protecting the security of information systems, as the frequency and complexity of network attacks are increasing with the rapid development of the Internet. Recent research studies have proposed various neural network models for NID, but they need to manually design the network architectures based on expert knowledge, which is very time-consuming. To solve this problem, this paper proposes a Multi-objective Evolutionary Neural Architecture Search (MENAS) method, which can automatically design neural network models for NID. First, a comprehensive search space is designed and then a weight-sharing mechanism is used to construct a supernet for NID, allowing each subnet to inherit weights from the supernet for direct performance evaluation. Subsequently, the subnets are encoded as chromosomes for multi-objective evolutionary search, which simultaneously optimizes two objectives: enhancing the model’s detection performance and reducing its complexity. To improve the search capability, a path-based crossover method is designed, which can iteratively refine the subnets’ architectures by simultaneously optimizing their accuracy and complexity for NID. At last, our MENAS method has been validated through extensive experiments on three well-known NID datasets: NSL-KDD, UNSW-NB15, and CICIDS2017. The experiments show that our MENAS method obtains an average 1.45% improvement on accuracy and an average 68.70% reduction on floating-point operations through multi-objective optimization process on six scenarios, which outperforms some state-of-the-art NID methods.

Multi-Task Multi-Objective Evolutionary Search Based on Deep Reinforcement Learning for Multi-Objective Vehicle Routing Problems with Time Windows

The vehicle routing problem with time windows (VRPTW) is a widely studied combinatorial optimization problem in supply chains and logistics within the last decade. Recent research has explored the potential of deep reinforcement learning (DRL) as a promising solution for the VRPTW. However, the challenge of addressing the VRPTW with many conflicting objectives (MOVRPTW) still remains for DRL. The MOVRPTW considers five conflicting objectives simultaneously: minimizing the number of vehicles required, the total travel distance, the travel time of the longest route, the total waiting time for early arrivals, and the total delay time for late arrivals. To tackle the MOVRPTW, this study introduces the MTMO/DRP-AT, a multi-task multi-objective evolutionary search algorithm, by making full use of both DRL and the multitasking mechanism. In the MTMO/DRL-AT, a two-objective MOVRPTW is constructed as an assisted task, with the objectives being to minimize the total travel distance and the travel time of the longest route. Both the main task and the assisted task are simultaneously solved in a multitasking scenario. Each task is decomposed into scalar optimization subproblems, which are then solved by an attention model trained using DRL. The outputs of these trained models serve as the initial solutions for the MTMO/DRL-AT. Subsequently, the proposed algorithm incorporates knowledge transfer and multiple local search operators to further enhance the quality of these promising solutions. The simulation results on real-world benchmarks highlight the superior performance of the MTMO/DRL-AT compared to several other algorithms in solving the MOVRPTW.

XtalOpt version 13: Multi-objective evolutionary search for novel functional materials

Version 13 of XtalOpt, an evolutionary algorithm for crystal structure prediction, is now available for download from the CPC program library or the XtalOpt website, https://xtalopt.github.io. In the new version of the XtalOpt code, a general platform for multi-objective global optimization is implemented. This functionality is designed to facilitate the search for (meta)stable phases of functional materials through minimization of the enthalpy of a crystalline system coupled with the simultaneous optimization of any desired properties that are specified by the user. The code is also able to perform a constrained search by filtering the parent pool of structures based on a user-specified feature, while optimizing multiple objectives. Here, we present the implementation and various technical details, and we provide a brief overview of additional improvements that have been introduced in the new version of XtalOpt. Program summaryProgram Title: XtalOptCPC Library link to program files:https://doi.org/10.17632/jt5pvnnm39.4Developer's repository link:https://github.com/xtalopt/XtalOptLicensing provisions: BSD 3-clauseProgramming language: C++.Journal reference of previous version: Comput. Phys. Commun. 237 (2019) 274–275.Does the new version supersede the previous version?: Yes.Reasons for the new version: Implementation of a multi-objective evolutionary search within the XtalOpt program package.Summary of revisions: Implemented a general user-friendly multi-objective search capability, made various improvements to user interface and functionalities, performed bug fixes.Nature of problem: The XtalOpt algorithm is designed to search for (meta)stable crystal structures, optionally with specific functionalities – a grand challenge in computational materials science, chemistry and physics.Solution method: A generalized scalar fitness function, where a set of user-specified objectives contribute to the fitness value for candidate structures, is implemented within XtalOpt. This generalized fitness biases the search towards the discovery of (meta)stable phases with structural motifs that are key for the desired characteristics. As a result, the evolutionary search explores regions of the energy landscape of higher relevance in terms of target properties.

Multi-objective evolutionary search of variable-length composite semantic perturbations

Deep neural networks have proven to be vulnerable to adversarial attacks in the form of adding specific perturbations on images to make wrong outputs. Among that, semantic perturbations attract increasing attention due to their naturalness and physical realizability. However, existing works about semantic perturbations have two limitations. On the one hand, the type of devised semantic perturbations is single, which easily causes poor attack performance, especially on the corresponding defensed models. On the other hand, composite semantic perturbations (CSP) improve the attack performance by integrating multiple semantic perturbations but lacks the role of automatic optimization, such as the attack order, which also leads to relatively low generalizability. To address these problems, we propose a novel method called multi-objective evolutionary search of variable-length composite semantic perturbations (MES-VCSP). Specifically, we construct the mathematical model of variable-length CSP, which allows the same type of attacks to be performed multiple times, realizing a more efficient attack. Besides, we introduce the multi-objective evolutionary search consisting of NSGA-II and neighborhood search to find near-optimal variable-length attack sequences. Experimental results on multiple image classification datasets show that compared with CSP, MES-VCSP can obtain adversarial examples with a higher attack success rate, more naturalness, and less time cost.

E2SCNet: Efficient Multiobjective Evolutionary Automatic Search for Remote Sensing Image Scene Classification Network Architecture.

Remote sensing image scene classification methods based on deep learning have been widely studied and discussed. However, most of the network architectures are directly reliant on natural image processing methods and are fixed. A few studies have focused on automatic search mechanisms, but they cannot weigh the interpretation accuracy and the parameter quantity for practical application. As a result, automatic global search methods based on multiobjective evolutionary computation have more advantages. However, in the ranking process, the network individuals with large parameter quantities are easy to eliminate, but a higher accuracy may be obtained after full training. In addition, evolutionary neural architecture search methods often take several days. In this article, in order to solve the above concerns, we propose an efficient multiobjective evolutionary automatic search framework for remote sensing image scene classification deep learning network architectures (E2SCNet). In E2SCNet, eight kinds of lightweight operators are used to build a diversified search space, and the coding connection mode is flexible. In the search process, a large model retention mechanism is implemented through two-step multiobjective modeling and evolutionary search, where one step involves the "parameter quantity and accuracy," and the other step involves the "parameter quantity and accuracy growth quantity." Moreover, a super network is constructed to share the weight in the process of individual network evaluation and promote the search speed. The effectiveness of E2SCNet is proven by comparison with several networks designed by human experts and networks obtained by gradient and evolutionary computing-based search methods.

Third-party software library migration at the method-level using multi-objective evolutionary search

Software developers commonly use third-party software libraries to reduce implementation efforts and mitigate errors in their source code while building high-quality and reliable software. To support software evolution, newer libraries are released periodically with added features, improvements in performance as well as critical updates such as bug fixes. Hence, older, existing (source) libraries need to be replaced with their newer, updated (target) counterparts, in a process known as library migration. Typically, library migration is a time-consuming, manual, and error-prone process that requires developers to analyze the source and target library code and its documentation. Specifically, developers examine Application Programming Interface (API) implementations and documentation to replace each source API with a target API without modifying the underlying software functionality. While recent works have used various techniques to recommend suitable target library replacements, these approaches do not generalize well, e.g., when a source library method needs to be replaced by one or more target library APIs or methods. To address this limitation, we propose the use of multi-objective evolutionary algorithms to identify suitable method replacements during library migration. In particular, we formulate library migration at the method-level as a multi-objective combinatorial optimization problem and examine the performance of 7 multi-objective evolutionary algorithms: UNSGAIII, RNSGAII, AGEMOEA, SMSEMOA, NSGAII, IBEA and MOEAD. We use method signature and documentation similarity, and co-occurrence probability to accurately recommend one or more target library methods. We evaluate our approach by conducting an empirical study on 9 popular Java library migrations mined from 57,447 open-source projects on GitHub. Our results demonstrate that UNSGAIII, RNSGAII, AGEMOEA, SMSEMOA, NSGAII, IBEA and MOEAD achieve 90%, 89%, 94%, 90%, 91%, 94%, and 71% precision on average, and 83%, 23%, 58%, 63%, 58%, 60% and 17% average recall respectively. In the interest of reproducibility, we make all code and results publicly available at: http://bit.ly/MOO-api-migration.

Learning Regularity for Evolutionary Multiobjective Search: A Generative Model-Based Approach

Learning Regularity for Evolutionary Multiobjective Search: A Generative Model-Based Approach

Solving an energy resource management problem with a novel multi-objective evolutionary reinforcement learning method

Microgrids have become popular candidates for integrating diverse energy sources into the power grid as means of reducing fossil fuel usage. Energy Resource Management (ERM) is a type of Unit Commitment problem, where a player operates a microgrid with diverse renewable generators integrated with an external supplier. Calculating the economic dispatch of each committed unit on a planning horizon is an NP-hard problem, and therefore, finding an exact solution is difficult. This paper presents a multi-objective solution to the ERM problem from the perspective of battery operation and external supplier dispatch. First, a novel multi-objective decision problem modeling is proposed that considers three objectives: cost, greenhouse gas emissions, and battery degradation. This framework involves a learning agent that controls the depth of discharge of a Lithium-Ion battery. To address the proposed problem, a new multi-objective algorithm called Multi-Objective Evolutionary Policy Search (MEPS) is introduced. The proposed algorithm uses NeuroEvolution of Augmenting Topologies structure to evolve artificial neural networks for estimating action-preference values considering multi-objective rewards. The MEPS performance is evaluated on both standard and newly-proposed benchmark problems, using the hypervolume as the evaluation metric. When compared to standard deep reinforcement learning, results showed that MEPS provides cost-effective, environmentally friendly, and efficient energy storage management solutions. Furthermore, MEPS effectively solves the proposed ERM problem by finding neural networks with a small number of nodes and connections, which are suitable for use in embedded control systems. Overall, MEPS proved to be a promising multi-objective approach in the transition to clean energy resources.

Bi-fidelity evolutionary multiobjective search for adversarially robust deep neural architectures

Deep neural networks have been found vulnerable to adversarial attacks, thus raising potential concerns in security-sensitive contexts. To address this problem, recent research has investigated the adversarial robustness of deep neural networks from the architectural point of view. However, searching for architectures of deep neural networks is computationally expensive, particularly when coupled with an adversarial training process. To meet the above challenge, this paper proposes a bi-fidelity multiobjective neural architecture search approach. First, we formulate the neural architecture search (NAS) problem for enhancing the adversarial robustness of deep neural networks into a multiobjective optimization problem. Specifically, in addition to using low-fidelity estimations as the primary objectives, we leverage the output of a surrogate model trained with high-fidelity evaluations as an auxiliary objective. Secondly, we reduce the computational cost by combining three performance estimation methods, i.e., parameter sharing, low-fidelity evaluation, and surrogate-based predictor. The effectiveness of the proposed approach is confirmed by extensive experiments conducted on CIFAR-10, CIFAR-100 and SVHN datasets.

BPEL process defects prediction using multi-objective evolutionary search

Web services are becoming increasingly popular technologies for modern organizations to improve their cooperation and collaboration through building new software systems by composing pre-built services. Such services are typically composed and executed through BPEL (Business Process Execution Language) processes. Like any other software artifact, such processes are frequently changed to add new or modify existing functionalities or adapt to environmental changes. However, poorly planned changes may introduce BPEL process design defects known as anti-patterns or defects. The presence of defects often leads to a regression in software quality. In this paper, we introduce an automated approach to predict the presence of defects in BPEL code using Multi-Objective Genetic Programming (MOGP). Our approach consists of learning from real-world instances of each service-based business process defect (i.e., anti-pattern) type to infer prediction rules based on the combinations of process metrics and their associated threshold values. We evaluate our approach based on a dataset of 178 real-world business processes that belong to various application domains, and a variety of BPEL process defect types such as data flow and portability defects. The statistical analysis of the achieved results shows the effectiveness of our approach in identifying defects compared with state-of-the-art techniques with a median accuracy of 91%.

Multi-Objective Evolutionary Architecture Search for Parameterized Quantum Circuits

Recent work on hybrid quantum-classical machine learning systems has demonstrated success in utilizing parameterized quantum circuits (PQCs) to solve the challenging reinforcement learning (RL) tasks, with provable learning advantages over classical systems, e.g., deep neural networks. While existing work demonstrates and exploits the strength of PQC-based models, the design choices of PQC architectures and the interactions between different quantum circuits on learning tasks are generally underexplored. In this work, we introduce a Multi-objective Evolutionary Architecture Search framework for parameterized quantum circuits (MEAS-PQC), which uses a multi-objective genetic algorithm with quantum-specific configurations to perform efficient searching of optimal PQC architectures. Experimental results show that our method can find architectures that have superior learning performance on three benchmark RL tasks, and are also optimized for additional objectives including reductions in quantum noise and model size. Further analysis of patterns and probability distributions of quantum operations helps identify performance-critical design choices of hybrid quantum-classical learning systems.

Detecting Continuous Integration Skip Commits Using Multi-Objective Evolutionary Search

Continuous Integration (CI) consists of integrating the changes introduced by different developers more frequently through the automation of build process. Nevertheless, the CI build process is seen as a major barrier that causes delays in the product release dates. One of the main reasons for such delays is that some simple changes (i.e., can be skipped) trigger the build, which represents an unnecessary overhead and particularly painful for large projects. In order to cut off the expenses of CI build time, we propose in this paper, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SkipCI</small> , a novel search-based approach to automatically detect CI Skip commits based on the adaptation of Strength-Pareto Evolutionary Algorithm (SPEA-2). Our approach aims to provide the optimal trade-off between two conflicting objectives to deal with both skipped and non-skipped commits. We evaluate our approach and investigate the performance of both within and cross-project validations on a benchmark of 14,294 CI commits from 15 projects that use Travis CI system. The statistical tests revealed that our approach shows a clear advantage over the baseline approaches with average scores of 92% and 84% in terms of AUC for cross-validation and cross-project validations respectively. Furthermore, the features analysis reveals that documentation changes, terms appearing in the commit message and the committer experience are the most prominent features in CI skip detection. When it comes to the cross-project scenario, the results reveal that besides the documentation changes, there is a strong link between current and previous commits results. Moreover, we deployed and evaluated the usefulness of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SkipCI</small> with our industrial partner. Qualitative results demonstrate the effectiveness of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SkipCI</small> in providing relevant CI skip commit recommendations to developers for two large software projects from practitioner’s point of view.

Coarse-to-fine evolutionary search for large-scale multi-objective optimization: An application to ratio error estimation of voltage transformers

Multi-objective optimization problems (MOPs) are commonly confronted in various fields, such as condition monitoring for renewable energy systems, and ratio error estimation of voltage transformers. With the increase in decision variables of MOPs, their exponentially growing search spaces are challenging for existing evolutionary algorithms. To handle this challenge, this paper suggests a coarse-to-fine large-scale evolutionary multi-objective search, called CF-LEMO. In the coarse search phase, CF-LEMO performs evolutionary search on both the original and transformed large-scale MOPs alternately, accelerating the population to approach the Pareto-optimal fronts. In addition, to alleviate the issue of diversity loss, we design a diversity preservation mechanism to preserve a well-distributed archive to support subsequent fine search. In the fine search stage, CF-LEMO conducts local search on the current population to mine high-quality solutions, which are used to update the population and archive. Then, based on the archive, the multi-objective optimization based on decomposition is employed to evolve all decision variables, so as to obtain a population with good convergence and diversity near the Pareto-optimal fronts. To assess the effectiveness of the proposed CF-LEMO, we compare its performance against four representative baseline algorithms on a benchmarks suite LSMOP1-LSMOP9 with 2 and 3 objectives. The empirical results confirm its super performance by significanlty outperforming all the four competitors on 12 out of 18 benchmarks. Moreover, the experiments demonstrate the superior performance of CF-LEMO in sovling multi-objective ratio error estimation problems with up to 6,000 decision variables.

How Climate Extremes Influence Conceptual Rainfall-Runoff Model Performance and Uncertainty

Rainfall-runoff models are frequently used for assessing climate risks by predicting changes in streamflow and other hydrological processes due to anticipated anthropogenic climate change, climate variability, and land management. Historical observations are commonly used to calibrate empirically the performance of conceptual hydrological mechanisms. As a result, calibration procedures are limited when extrapolated to novel climate conditions under future scenarios. In this paper, rainfall-runoff model performance and the simulated catchment hydrological processes were explored using the JAMS/J2000 model for the Berg River catchment in South Africa to evaluate the model in the tails of the current distribution of climatic conditions. An evolutionary multi-objective search algorithm was used to develop sets of parameters which best simulate “wet” and “dry” periods, providing the upper and lower bounds for a temporal uncertainty analysis approach to identify variables which are affected by these climate extremes. Variables most affected included soil-water storage and timing of interflow and groundwater flow, emerging as the overall dampening of the simulated hydrograph. Previous modeling showed that the JAMS/J2000 model provided a “good” simulation for periods where the yearly long-term mean precipitation shortfall was &amp;lt;28%. Above this threshold, and where autumn precipitation was reduced by 50%, this paper shows that the use of a set of “dry” parameters is recommended to improve model performance. These “dry” parameters better account for the change in streamflow timing of concentration and reduced peak flows, which occur in drier winter years, improving the Nash-Sutcliffe Efficiency (NSE) from 0.26 to 0.60 for the validation period 2015–2018, although the availability of climate data was still a potential factor. As the model performance was “good” (NSE &amp;gt; 0.7) during “wet” periods using parameters from a long-term calibration, “wet” parameters were not recommended for the Berg River catchment, but could play a large role in tropical climates. The results of this study are likely transferrable to other conceptual rainfall/runoff models, but may differ for various climates. As greater climate variability drives hydrological changes around the world, future empirically-based hydrological projections need to evaluate assumptions regarding storage and the simulated hydrological processes, to enhanced climate risk management.

Simultaneous optimization of economic, environmental and safety criteria for algal biodiesel process retrofitted using dividing wall column and multistage vapor recompression

The present study deals with the multiobjective optimization (MOO) of retrofitted in situ algal biodiesel process. Transesterification of the algal lipids is intensified using ultrasonication and catalyzed using the ionic liquid catalyst. Process includes the retrofitting of two conventional distillation columns into a dividing wall column (DWC), which is further intensified using multistage vapor recompression (DWC-MVR) in order to decrease the energy consumption and CO2 emission from the process. Excel based hybridised multiobjective differential evolutionary dynamic local search (HMODE-DLS) algorithm is used for the constrained MOO, whereas Aspen Plus is used for the process development. Break even cost (BEC), eco indicator (EI99) and individual risk (IR) are considered as objectives to evaluate economic, environmental impact, and safety of process, respectively. Initially, bi-objective case studies were analyzed and finally, all three objectives are studied in one case. Pareto optimal solutions obtained from HMODE-DLS algorithm are then ranked by the simple additive weighted method to find out the best solution. MOO resulted in the significant decrease in BEC (~20%), EI99 (~48%) and IR (~10%).

Pareto optimal directional drilling advisory for improved real-time decision making

Directional drilling decision making is complicated by the presence of multiple objectives and constraints. Existing directional drilling advisory methods find a balanced solution through optimization over a weighted sum of the objectives, but the weights are often chosen arbitrarily or based on rough estimates. In addition, existing methods only return a single solution to a multi-objective problem that likely does not have a single, best solution. The work presented here overcomes these limitations by identifying a set of feasible, diversely performing (i.e., Pareto optimal) solutions for the directional driller to choose from. In this novel approach to directional drilling steering optimization, the directional drilling steering problem is framed as a constrained optimal control problem over a finite horizon. A wellbore propagation model that is calibrated at set interval using field data was used to estimate system responses for a bent-sub mud motor drilling assembly. Pareto front approximation was used to solve the problem and a multi-objective evolutionary search method was leveraged to solve this optimization problem and present the directional driller with a set of Pareto optimal solutions that satisfy the problem constraints. The proposed Pareto front approximation approach was validated against a set of field test cases. These demonstrate that the proposed method is capable of finding feasible, high-performing solutions to these real-world problems sufficiently fast to be of use for real-time directional drilling advisory in the field. • First application of Pareto front approximation to the directional drilling steering optimization problem.

Multivariate Feature Ranking With High-Dimensional Data for Classification Tasks

In many machine learning classification problems, datasets are usually of high dimensionality and therefore require efficient and effective methods for identifying the relative importance of their attributes, eliminating the redundant and irrelevant ones. Due to the huge size of the search space of the possible solutions, the attribute subset evaluation feature selection methods are not very suitable, so in these scenarios feature ranking methods are used. Most of the feature ranking methods described in the literature are univariate methods, which do not detect interactions between factors. In this paper, we propose two new multivariate feature ranking methods based on pairwise correlation and pairwise consistency, which have been applied for cancer gene expression and genotype-tissue expression classification tasks using public datasets. We statistically proved that the proposed methods outperform the state-of-the-art feature ranking methods <i>Clustering Variation, Chi Squared, Correlation, Information Gain, ReliefF</i> and <i>Significance</i>, as well as other feature selection methods for attribute subset evaluation based on correlation and consistency with the multi-objective evolutionary search strategy, and with the embedded feature selection methods <i>C4.5</i> and <i>LASSO</i>. The proposed methods have been implemented on the WEKA platform for public use, making all the results reported in this paper repeatable and replicable.

Improving Search-Based Automatic Program Repair With Neural Machine Translation

The challenge of automatically repairing bugs in programs to reduce debugging expenses and increase program quality is known as automated program repair. To overcome this issue, test-suite-based repair techniques use a specified test suite as an oracle and alter the input faulty program to pass the full test suite. GenProg is a well-known example of this kind of repair, in which genetic programming is used to reorder the statements already present in the faulty program. However, recent practical experiments suggest that GenProg’s performance, notably for Java, is not sufficient. Improved program dependability necessitates the use of automatic program repair techniques. Template-based program repair techniques have recently been combined with search-based techniques to solve program issues automatically. Although intriguing, it has two fundamental drawbacks: Its search space often lacks the correct solution, and the technique disregards program expertise, such as precise code language. Compared with the template-based program repair approach, existing neural-machine-translation-based approaches are not limited by these constraints due to their ability to learn and generate new solutions. We propose an approach that combines a search-based automatic program repair technique with a neural-machine-translation-based approach. More specifically, we use both redundancy assumption and sequence-to-sequence learning of correct patches as the source for potential fix statements that feed into a multiobjective evolutionary search algorithm to find test-suite-adequate patches. In this work, a novel framework called ARJANMT is introduced for automatically repairing Java programs. Two sets of controlled experiments are conducted on 410 bugs from two benchmarks to investigate the repairability and correctness of our proposed framework. A comparison between state-of-the-art automatic program repair frameworks is made. The experimental results indicate that combining those two types of repair techniques (search-based and neural-machine-translation-based) produces better results or fixes bugs that they previously were unable to fix individually.

Multi-objective evolutionary search strategies in constraint programming

It has been shown that evolutionary algorithms are able to construct suitable search strategies for classes of Constraint Satisfaction Problems (CSPs) in Constraint Programming. This paper is an explanation of the use of multi-objective optimisation in contrast to simple additive weighting techniques with a view to develop search strategies to classes of CSPs. A hierarchical scheme is employed to select a candidate strategy from the Pareto frontier for final evaluation. The results demonstrate that multi-objective optimisation significantly outperforms the single objective scheme in the same number of objective evaluations. In situations where strategies developed for a class of problems fail to extend to unseen problem instances of the same class, it is found that the structure of the underlying CSPs do not resemble those employed in the training process.

Modeling and Forecasting Gender-Based Violence through Machine Learning Techniques

Gender-Based Violence (GBV) is a serious problem that societies and governments must address using all applicable resources. This requires adequate planning in order to optimize both resources and budget, which demands a thorough understanding of the magnitude of the problem, as well as analysis of its past impact in order to infer future incidence. On the other hand, for years, the rise of Machine Learning techniques and Big Data has led different countries to collect information on both GBV and other general social variables that in one way or another can affect violence levels. In this work, in order to forecast GBV, firstly, a database of features related to more than a decade’s worth of GBV is compiled and prepared from official sources available due to Spain’s open access. Then, secondly, a methodology is proposed that involves testing different methods of features selection so that, with each of the subsets generated, four techniques of predictive algorithms are applied and compared. The tests conducted indicate that it is possible to predict the number of GBV complaints presented to a court at a predictive horizon of six months with an accuracy (Root Median Squared Error) of 0.1686 complaints to the courts per 10,000 inhabitants—throughout the whole Spanish territory—with a Multi-Objective Evolutionary Search Strategy for the selection of variables, and with Random Forest as the predictive algorithm. The proposed methodology has also been successfully applied to three specific Spanish territories of different populations (large, medium, and small), pointing to the presented method’s possible use elsewhere in the world.