Decision trees are widely used in machine learning due to their simplicity and interpretability, but they often lack robustness to adversarial attacks and data perturbations. The paper proposes a novel island-based coevolutionary algorithm (ICoEvoRDF) for constructing robust decision tree ensembles. The algorithm operates on multiple islands, each containing populations of decision trees and adversarial perturbations. The populations on each island evolve independently, with periodic migration of top-performing decision trees between islands. This approach fosters diversity and enhances the exploration of the solution space, leading to more robust and accurate decision tree ensembles. ICoEvoRDF utilizes a popular game theory concept of mixed Nash equilibrium for ensemble weighting, which further leads to improvement in results. ICoEvoRDF is evaluated on 20 benchmark datasets, demonstrating its superior performance compared to state-of-the-art methods in optimizing both adversarial accuracy and minimax regret. The flexibility of ICoEvoRDF allows for the integration of decision trees from various existing methods, providing a unified framework for combining diverse solutions. Our approach offers a promising direction for developing robust and interpretable machine learning models.

This paper introduces the Augmented Decision Space Optimization (ADSO) method for sparsity-driven optimization of mixed-strategies in Stackelberg Security Games (SSGs). The proposed method enhances traditional strategy optimization by combining binary variables to represent the presence of pure strategies with real-valued variables to refine their selection probabilities. Specifically, instead of waiting for an evolutionary process to gradually discover sparse solutions, the binary variables in ADS allow the real-valued variables to be switched on or off, thereby directly enforcing sparsity. This dual codification scheme achieves targets such as sparsification and computational efficiency in large-scale games. We demonstrate that ADS outperforms existing heuristic methods, offering superior solution quality, scalability, and stability. Empirical results across three different benchmark games show that ADS generates compact strategies with minimal computational overhead, achieving performance close to the exact methods. Furthermore, state-of-the-art results are obtained for problems where exact methods fail to scale effectively. Our framework promises broad applicability beyond SSGs, encompassing a wide range of game-theoretic and combinatorial optimization problems.

The paper introduces a novel hybrid island-based framework in which diverse metaheuristics cooperate to effectively explore the search space. A core component of the framework is a diversity-driven migration mechanism, enabling adaptive management of the information flow between islands. Three fundamental aspects of migration - what to migrate, when to migrate, and where to migrate - are thoroughly analyzed, leading to the development of strategies that foster synergy between heterogeneous algorithms. These strategies balance exploration and exploitation, ensuring effective global and local search. The framework was evaluated on a set of diverse optimization benchmarks, both discrete (Traveling Salesman Problem instances) and continuous (BBOB functions). Experimental results demonstrate that the proposed approach surpasses traditional algorithms and their island-based variants in convergence speed, solution quality, and resilience to stagnation. Adaptive mechanisms dynamically adjust migration strategies during the optimization process, further enhancing the framework's effectiveness. The proposed method represents an advancement in hybrid metaheuristic systems, offering scalability and flexibility that are essential for solving complex optimization tasks.

The optimization of complex problems remains a significant challenge across various domains of science and engineering. This paper introduces a novel approach to island-based optimization that dynamically adapts metaheuristic selection during runtime, extending the Diversity-driven Cooperating Portfolio of Metaheuristics (DdCPM) framework. Our method integrates additional metaheuristics beyond the original implementation and proposes adaptation strategies that dynamically reconfigure the algorithm portfolio based on performance indicators and population characteristics. Experimental results across both discrete and continuous optimization benchmarks demonstrate that adaptive metaheuristic selection enhances solution quality and convergence rates compared to static approaches. The proposed framework represents an advancement in hybrid optimization systems, offering improved performance through intelligent adaptation mechanisms that correspond to the evolving state of the search process.

In recent years, there has been growing interest in developing robust machine learning (ML) models that can withstand adversarial attacks, including one of the most widely adopted, efficient, and interpretable ML algorithms—decision trees (DTs). This paper proposes a novel coevolutionary algorithm (CoEvoRDT) designed to create robust DTs capable of handling noisy high-dimensional data in adversarial contexts. Motivated by the limitations of traditional DT algorithms, we leverage adaptive coevolution to allow DTs to evolve and learn from interactions with perturbed input data. CoEvoRDT alternately evolves competing populations of DTs and perturbed features, enabling construction of DTs with desired properties. CoEvoRDT is easily adaptable to various target metrics, allowing the use of tailored robustness criteria such as minimax regret. Furthermore, CoEvoRDT has potential to improve the results of other state-of-the-art methods by incorporating their outcomes (DTs they produce) into the initial population and optimize them in the process of coevolution. Inspired by the game theory, CoEvoRDT utilizes mixed Nash equilibrium to enhance convergence. The method is tested on 20 popular datasets and shows superior performance compared to 4 state-of-the-art algorithms. It outperformed all competing methods on 13 datasets with adversarial accuracy metrics, and on all 20 considered datasets with minimax regret. Strong experimental results and flexibility in choosing the error measure make CoEvoRDT a promising approach for constructing robust DTs in real-world applications.

One of the relevant aspects of Artificial General Intelligence is the ability of machines to demonstrate abstract reasoning skills, for instance, through solving (human) IQ tests. This work presents a new approach to machine IQ tests solving formulated as Raven’s Progressive Matrices (RPMs), called Duel-IQ. The proposed solution incorporates the concept of a tournament in which the best answer is chosen based on a set of duels between candidate RPM answers. The three relevant aspects are: (1) low computational and design complexity, (2) proposition of two schemes of pairing up candidate answers for the duels and (3) evaluation of the system on a dataset of shapes other than those used for training. Depending on a particular variant, the system reaches up to $82.8%$ accuracy on average in RPM tasks with 5 candidate answers and is on par with human performance and superior to other literature approaches of comparable complexity when training and test sets are from the same distribution.

Green Security Games have become a popular way to model scenarios involving the protection of natural resources, such as wildlife. Sensors (e.g. drones equipped with cameras) have also begun to play a role in these scenarios by providing real-time information. Incorporating both human and sensor defender resources strategically is the subject of recent work on Security Games with Signaling (SGS). However, current methods to solve SGS do not scale well in terms of time or memory. We therefore propose a novel approach to SGS, which, for the first time in this domain, employs an Evolutionary Computation paradigm: EASGS. EASGS effectively searches the huge SGS solution space via suitable solution encoding in a chromosome and a specially-designed set of operators. The operators include three types of mutations, each focusing on a particular aspect of the SGS solution, optimized crossover and a local coverage improvement scheme (a memetic aspect of EASGS). We also introduce a new set of benchmark games, based on dense or locally-dense graphs that reflect real-world SGS settings. In the majority of 342 test game instances, EASGS outperforms state-of-the-art methods, including a reinforcement learning method, in terms of time scalability, nearly constant memory utilization, and quality of the returned defender's strategies (expected payoffs).

The paper proposes a novel neuroevolutionary method (NESG) for calculating leader's payoff in Stackelberg Security Games. The heart of NESG is strategy evaluation neural network (SENN). SENN is able to effectively evaluate leader's strategies against an opponent who may potentially not behave in a perfectly rational way due to certain cognitive biases or limitations. SENN is trained on historical data and does not require any direct prior knowledge regarding the follower's target preferences, payoff distribution or bounded rationality model. NESG was tested on a set of 90 benchmark games inspired by real-world cybersecurity scenario known as deep packet inspections. Experimental results show an advantage of applying NESG over the existing state-of-the-art methods when playing against not perfectly rational opponents. The method provides high quality solutions with superior computation time scalability. Due to generic and knowledge-free construction of NESG, the method may be applied to various real-life security scenarios.

This paper introduces a new type of Security Games (SG) played on a plane with targets moving along predefined straight line trajectories and its respective Mixed Integer Linear Programming (MILP) formulation. Three approaches for solving the game are proposed and experimentally evaluated: application of an MILP solver to finding exact solutions for small-size games, MILP-based extension of recently published zero-sum SG approach to the case of generalsum games for finding approximate solutions of medium-size games, and the use of Memetic Algorithm (MA) for mediumsize and large-size game instances, which are beyond MILP’s scalability. Utilization of MA is, to the best of our knowledge, a new idea in the field of SG. The novelty of proposed solution lies specifically in efficient chromosome-based game encoding and dedicated local improvement heuristics. In vast majority of test cases with known equilibrium profiles, the method leads to optimal solutions with high stability and approximately linear time scalability. Another advantage is an iteration-based construction of the system, which makes the approach essentially an anytime method. This property is of paramount importance in case of restrictive time limits, which could hinder the possibility of calculating an exact solution. On a general note, we believe that MA-based methods may offer a viable alternative to MILP solvers for complex games that require application of approximate solving methods.

A new neural network method for Dimensionality Reduction (DR) of the input feature space in Multilabel Classification (MC) problems is proposed and experimentally evaluated in this paper. The method (abbreviated as TCART-MR) can be used in two possible scenarios: either as a stand-alone DR pre-processing phase, preceding subsequent application of any particular MC algorithm, or as a compact MC approach in which TCART-MR is applied twice - first to DR task and then to MC problem with reduced input space. Extensive experimental results proved statistically relevant advantage of TCART-MR over three state-of-the-art approaches in DR domain (in the context of MC), as well as its superiority over 10 state-of-the-art MC algorithms listed in a recent MC survey paper. The MC tests were performed on a set of 9 benchmark problems and 16 evaluation measures (leading to 144 experimental cases in total).

This paper presents a neural network approach to solving the most common type of human IQ test problems - Raven's Progressive Matrices (RMs). The proposed DeepIQ system is composed of three modules: a deep autoencoder which is trained to learn a feature-based representation of various figure images used in IQ tests, an ensemble of shallow multilayer perceptrons applied to detection of feature differences, and a scoring module use for assessment of candidate answers. DeepIQ is able to learn the underlying principles of solving RMs (the importance of similarity of figures in shape, rotation, size or shading) in a domain-independent way, that allows its subsequent application to test instances constructed based on a different set of figures, never seen before, or another type of IQ problem, with no requirement for additional training. This transfer learning property is of paramount importance due to scarce availability of the real data, and is demonstrated in the paper on two different RM data sets, as well as two distinct types of IQ tasks (solving RMs and odd-one-out problems). Experimental results are promising, excelling human average scores by a large margin on the most challenging subset of RM instances and exceeding 90% accuracy in odd-one-out tests.

In 2006 Zhang and Zhou proposed a multilabel classification model based on the MLP network, which was subsequently improved by Grodzicki et al. This paper further improves both these approaches by introducing a scaling parameter responsible for maintaining a balance between the impacts of particular components of the MLP's error function in the training process. The newly-proposed parameter is autonomously fine-tuned by the system in the nested cross validation process. The proposed approach is tested on a set of well-established benchmarks and demonstrates its superiority over the baseline methods for 16 different error measures used in the experiments. Furthermore, the method proves competitive to 12 other state-of-the-art machine learning approaches which are used for further comparisons. In the combined score composed of ranking positions for all benchmarks and all error functions, the proposed neural network system gains the leading position among all tested methods.