Effective visual representation learning is crucial for reinforcement learning (RL) agents to extract task-relevant information from raw sensory inputs and generalize across diverse environments. However, existing RL benchmarks lack the ability to systematically evaluate representation learning capabilities in isolation from other learning challenges. To address this gap, we introduce the Sliding Puzzles Gym (SPGym), a novel benchmark that transforms the classic 8-tile puzzle into a visual RL task with images drawn from arbitrarily large datasets. SPGym's key innovation lies in its ability to precisely control representation learning complexity through adjustable grid sizes and image pools, while maintaining fixed environment dynamics, observation, and action spaces. This design enables researchers to isolate and scale the visual representation challenge independently of other learning components. Through extensive experiments with model-free and model-based RL algorithms, we uncover fundamental limitations in current methods' ability to handle visual diversity. As we increase the pool of possible images, all algorithms exhibit in- and out-of-distribution performance degradation, with sophisticated representation learning techniques often underperforming simpler approaches like data augmentation. These findings highlight critical gaps in visual representation learning for RL and establish SPGym as a valuable tool for driving progress in robust, generalizable decision-making systems.

Node-Depth Based Encoding is a representation for Evolutionary Algorithms applied to problems modelled by trees, storing nodes and their respective depths in an appropriately ordered list. This representation was highlighted by the results obtained, whose mutation and recombination operators have low time complexity in relation to other representations for the same problems. This work proposes new search operators. A high locality mutation operator, having as its main differential the possibility of including any edge present in a graph and a recombination operator with the ability to generate solutions with as much heritability as possible considering the edges set in the solutions, making it possible to recombine any solutions in the population with the aim of improving search convergence. All proposed operators also allow for dealing with problems modelled by forests with many trees and complete or non-complete graphs, always generating feasible solutions. This study performed bias, locality and heritability investigations for proposed operators showing that they have adequate characteristics for dealing with high dimensionality problems. In order to evaluate the performance of proposed operators, the results of preliminary tests were obtained applying to a benchmark problem in the literature. The use of proposed operators resulted in faster convergence.

Ideally, in FSM-based testing, a test suite should have as few input symbols and sequences as possible, speeding up its execution, and being able to reveal as many faulty implementations as possible in a given fault domain. Unfortunately, in general, it is an NP-hard problem. Several test generation methods have been proposed to generate a test suite with full fault detection capability. However, they usually generate large test suites with many sequences. In this paper, we propose a search-based software testing method aiming to generate test suites that represent a good trade-off between its cost and its fault coverage. A new encoding, called Node-depth-outdegree Encoding (NDOE) and its mutation and initialization operators are proposed. Also, a new fitness function is presented. The proposed fitness is composed of three objectives: minimize the test suite length, minimize the number of test sequences, and maximize the fault coverage, which analysis is based on mutation score. The experimental results show that the proposed method is able to generate lower cost test suites, with high mutation score, than literature test generation methods.

Feature selection is a important tast to reduce dimensionality in large datasets. Datasets from multivariate calibration problems are a good example lf datasets with a large number of features. In literature, there are several types of techniques to reduce the number of features for this problem, among them, evolutionary algorithms such as genetic algorithms (GAs). They have been successfully used with binary encoding to select features in multivariate calibration. However, as far as we know, there is no work in literature which provides an integer encoding GA in such context. Thus, this paper presents an integer-based GA implementation for feature selection in multivariate calibration models. The results demonstrated that our proposal is able to outperform the outcomes of participants from 2014 IDRC regarding model prediction error as well as number of selected features. In this dataset, the samples correspond to oils from petroleum reservoirs around the world and gas mixtures in the gas phase measured in transmittance. The gain of our proposed implementation in relation to the winner was from 20.9% up to 88.8%.

Bio-inspired metaheuristics have been used to solve various types of problems. Firefly algorithm (FA) is a nature-inspired metaheuristic based on the flash characteristics of fireflies. Variations of the FA implementation have been applied in the solution of optimization problems, including the problem of variable selection. Some works in the literature have applied several types of strategy in FA to select uncorrelated informative variables. On the other hand, other works have presented the use of compact evolutionary strategy such as compact genetic algorithm (CGA). However, as far as we know, there is still none work that uses such approach in the FA. Therefore, this paper proposes a compact firefly algorithm (CFA) implementation for the variable selection problem in multivariate calibration models to pharmaceutical ingredient determination. In an experiment comparison, our outcomes demonstrate that CFA is able to construct an accurate model with a more reduced number of variables selected. Additionally, CFA is up to 245% better than a standard GA implementation.

This paper presents a multi-objective formulation for variable selection in calibration problems. The prediction of protein concentration on wheat is obtained by a linear regression model using variables obtained by a spectrophotometer device. This device measure hundreds of correlated variables related with physicochemical properties and that can be used to estimate the protein concentration. The problem is the selection of a subset informative and uncorrelated variables that help the minimization of prediction error. In this work we propose the use of two objectives in this problem: the prediction error and the number of variables in the model, both related to linear equations system stability. We proposed a multi-objective formulation using two multi-objective algorithms: the NSGA-II and the SPEA-II. Additionally we propose a final decision maker method to choice the final subset of variables from the Pareto front. For the case study is used wheat data obtained by NIR spectrometry where the objective is the determination of a variable subgroup with information about protein concentration. The results of traditional techniques of multivariate calibration as the Successive Projections Algorithm (SPA), Partial Least Square (PLS) and mono-objective genetic algorithm are presents for comparisons. For NIR spectral analysis of protein concentration on wheat, the number of variables selected from 775 spectral variables was reduced for just 10 in the SPEA-II algorithm. The prediction error decreased from 0.2 in the classical methods to 0.09 in proposed approach, a reduction of 45%. The model using variables selected by SPEA-II had better prediction performance than classical algorithms and full-spectrum partial least-squares (PLS).

The design of a network is a solution to several engineering and science problems. Several network design problems are known to be NP-hard, and population-based metaheuristics like evolutionary algorithms (EAs) have been largely investigated for such problems. Such optimization methods simultaneously generate a large number of potential solutions to investigate the search space in breadth and, consequently, to avoid local optima. Obtaining a potential solution usually involves the construction and maintenance of several spanning trees, or more generally, spanning forests. To efficiently explore the search space, special data structures have been developed to provide operations that manipulate a set of spanning trees (population). For a tree withnnodes, the most efficient data structures available in the literature require timeO(n) to generate a new spanning tree that modifies an existing one and to store the new solution. We propose a new data structure, called node-depth-degree representation (NDDR), and we demonstrate that using this encoding, generating a new spanning forest requires average timeO(√n). Experiments with an EA based on NDDR applied to large-scale instances of the degree-constrained minimum spanning tree problem have shown that the implementation adds small constants and lower order terms to the theoretical bound.

The node-depth encoding has elements from direct and indirect encoding for trees which encodes trees by storing the depth of nodes in a list. Node-depth encoding applies specific search operators that is a typical characteristic for direct encodings. An investigation into the bias of the initialization process and the mutation operators of the node-depth encoding shows that the initialization process has a bias to solutions with small depths and diameters, and a bias towards stars. This investigation, also, shows that the mutation operators are unbiased. The performance of node-depth encoding is investigated for the bounded-diameter minimum spanning tree problem. The results are presented for Euclidean instances presented in the literature. In contrast with the expectation, the evolutionary algorithm using the biased initialization operator does not allow evolutionary algorithms to find better solutions compared to an unbiased initialization. In comparison to other evolutionary algorithms for the bounded-diameter minimum spanning tree evolutionary algorithms using the node-depth encoding have a good performance.

Proteins are polymers whose chains are composed of 20 different monomers, called amino acids. The problem of Protein Structure Prediction (PSP) is the determination of protein 3D conformation from its amino acid sequence. Two main strategies are usually employed to work with PSP: homology and Ab initio approaches. This paper presents an Evolutionary Algorithm to PSP using an Ab initio approach (ProtPred). The predictions are evaluated using fitness functions based on potential energies (electrostatic and van der Waals) and hydrophobic interactions. The proposed approach uses dihedral angles and main angles of the lateral chains to model a protein structure. ProtPred is evaluated using relatively complex cases for an Ab initio approach. Results have shown that ProtPred is a consistent approach.

The Protein Structure Prediction (PSP) is to determine the proteintertiary structure from its amino acids. This paper presents the ProtPred and investigates its application. The first results showed that ProtPred is a consistent approach.

Network design involves several areas of research. Computer networks, electrical circuits and transportation problems are some examples. In order to deal with the complexity of these problems, approaches using evolutionary algorithms have been proposed for network design problems (NDPs) with relevant results. Nevertheless, the graph encoding is critical for the performance of evolutionary algorithms for NDPs. The node-depth encoding (NDE) has presented relevant results for NDPs involving undirected graphs. In this sense, this article proposes an extension of NDE for NDPs modeled by directed graphs.