Forest fires have increased as a result of climate change, causing severe environmental damage. Cellular Automata (CA) models can simulate fire spread, aiding decision-making, but their parameter tuning is complex. This research develops a Genetic Algorithm (GA) to automate CA parameter adjustment. The study evaluates different fitness functions and GA’s ability to reproduce the fire dynamics present in datasets produced under varying sampling intervals. The experimental results show that GA effectively tunes the parameters, achieving accurate fire propagation across models.

The literature contains several studies that use cellular automata models applied to pedestrian evacuation simulations. Some of these models use the floor field concept to model scenarios and indicate the position of exits. However, most of these studies are focused on indoor environments. This study develops a new model based on the Varas model, restricting determinism by including stochastic transitions (confusion parameter) and by including road signs to reduce the impact of the new parameter. Applied to maps based on outdoor scenarios, experiments show that the new model presents a significant performance improvement in the metrics of average evacuation time and average waiting time. In addition, the inclusion of road signs also presents a considerable improvement in the average evacuation time when the scenario has a complex configuration, such as isolated regions partially closed by obstacles. This regions are unsafe spaces for pedestrians to find an exit in an emergency situation.

Collective environmental mapping leverages the decentralised and cooperative principles of swarm robotics to efficiently explore and map complex environments. Swarm robotics is particularly advantageous in large-scale or dynamic scenarios, where centralised systems often face limitations related to coordination overhead and computational scalability. This work enhances a bio-inspired coordination model for robot swarms by integrating a queue-based Past-Path Memory. While the original model enables effective swarm distribution and periodic environment revisitation, the memory-enhanced adaptation allows robots to retain and share environmental data, promoting the emergence of a global mapping behaviour. Experimental results demonstrate that this adaptation significantly improves collective mapping efficiency, with performance gains ranging from 280% to 857% over the baseline model and scaling proportionally with Past-Path Memory size.

The dynamics of forest plantations have been widely studied with computational simulation applications. Cellular automata (CA) is a technique capable of modelling future states based on a set of transition rules. However, this construction is not simple, often requiring technical knowledge of the process through years of scientific research. Machine learning techniques can be applied in this context, facilitating the construction of these simulators. This work presents a simulation model based on probabilistic cellular automata capable of estimating the evolution of wood production throughout the management period. Unlike other works in the literature, the construction of the CA transition rule is based exclusively on historical data from a Tachi-branco plantation, a managed forest species. Linear and logistic regression models are applied to learn and represent the local transition rules of the automaton and simulate its evolution. The proposed CA-based approach was able to predict the future behavior of plantations in the monitored areas with errors around 4%, confirming the potential of using machine learning in discovering transition rules for precise models.

The design of peptides capable of inhibiting the SARS-CoV-2 viral infection has been considered one of the potential strategies to reduce the transmission of SARS-CoV-2. However, one critical issue in peptide design is the large search space, which makes it impracticable to evaluate all possibilities. Furthermore, most related analyses adopt in silico molecular docking to select potential peptides, which is a time-consuming technique and highly dependent on the molecular structure of already known peptides and the target protein. Aiming to assist the evaluation, discovery and selection of peptides for docking calculation, we developed SAGAPEP, a Surrogate-Assisted Genetic Algorithm framework capable of finding peptides with potential to block the SARS-Co V-2 Spike protein. The surrogate model is used for fast and high-fidelity evaluation of the interaction energy between a peptide and the Spike protein, while the genetic algorithm seeks to discover and select high-potential peptides inspired by principles of genetics and natural selection. Experiments were conducted using a data set composed of several potential peptides obtained through molecular docking by bio-informatics specialists. Our experimental results demonstrate that SAGAPEP achieved low error predictions from its surrogate component trained over that data set, and was able to discover and select peptides with higher binding energy than all present in the data set. Moreover, the noteworthy results of SAGAPEP suggest it may also have the potential to provide promising results for other peptide design problems.

Multiobjective Evolutionary Algorithms (MOEA’s) have been developed for optimization problems involving conflicting objectives. Real-world Batch Sequencing (BS) in pharmaceutical manufacturing presents a multiobjective optimization challenge, compounded by constraints and uncertain demands. This complexity often leads to low convergence of feasible solutions. In this study, we evaluate population initialization strategies, propose an optimized mutation operator, and explore various crossover types to enhance solution quality, measured by metrics including the number of non-dominated feasible solutions (NFS), hypervolume (HV), Inverted Generational Distance Plus (IGD+), Error Rate (E), and Coverage of Two Sets (CS).

An epidemiological model based on cellular automata (CA) rules is tuned through several parameters to provide a more accurate simulation of the real phenomena. CA are dynamic systems capable of describing complexity from simple components and local iterations. The parameters setting discussed here is guided by reference values that were obtained with real field data. We started from a recent study in which an adequate parameters configuration was sought for a stochastic CA-based epidemiological model of Chagas Disease through an evolutionary approach. The results were satisfactory but the performance of the standard genetic algorithm (GA) previously employed declines with the expansion of the search space. In order to improve performance, we present a multistage evolutionary strategy, where different settings are applied based on the current stage of the GA search. The proposed evolutionary approach provided solutions with the least error in the set of experiments, confirming the improvement over the previous approach.

Cellular automata (CA) are bio-inspired approach that have been recently investigated to several applications including robotics. An improved model based on CA rules is proposed and evaluated for path-planning in autonomous robots. The objective is to build a short collision-free path from the robot's starting position to the target, trying to avoid unnecessary turns as much as possible. CA rules are used to enlarge obstacles, avoid their concavities and spread the distance from each cell to the target. The robot route is planned using the information of the distance of each free cell to the goal. Experiments were carried out to confirm the efficiency of the new techniques employed. Simulations with the navigation of a e-puck robot using the Webots platform have shown promising results confirming that the model is able to plan smooth and short routes.

Reliable modeling allows the simulation of critical processes that can serve as a foundation for planning and defining public policies. Ecological, climatic, public health and epidemiological models, among others are important research instruments that can forecast and evaluate the impact of decisions made by organizations and governments. Once the basic representation of the process is defined, one of the main difficulties of modeling is the adjustment of several parameters that make up it. We investigate the application of genetic algorithms to adjust model parameters relying on data series as input since they consist in a powerful adaptive search method. The proposed approach is evaluated using a previous model based on probabilistic cellular automata that describes the evolution of a population of insect vectors responsible for Chagas disease. The experiments performed here shown that results of the evolutionary parameters adjustment are similar to the behavior of the reference model both in the quantity of insects and in their spatial distribution. Our approach achieved a robust error of 3.13, that is, a difference of approximately 3 insects in one-year simulation.

This work proposes a cellular automata-based model to solve a path-planning and formation control problem for a team of robots using local rules and discrete states. Planning collision-free trajectories is essential for autonomous robots when moving in unknown environments. The complexity of this task increases in multi-robot systems, in particular when all robots must adjust their path in order to keep their initial team formation pattern. Here, a decentralized and autonomous robot team path-planning model is proposed and tested. The proposed method is implemented on a simulation environment and on real e-puck robots. Results show improvements in the overall team efficiency and robustness in different scenarios using minimal robot-robot communication load.

A new model for many-objective optimization based on ant colony is presented. It decomposes a domain with several objectives into subdomains with pairs, triples and n-tuples of objectives. Pheromone structures related to each subdomain guide the ant population in the construction of non-dominated solutions for the different multiobjective subproblems, increasing the convergence to the Pareto Optimal of the original problem. The new algorithm was called Many-objective Ant Colony Optimization based on Non-Dominated Sets (MACO/NDS). The proposed model was evaluated in two discrete challenges: the multiobjective knapsack problem (MKP) and the multicast routing problem (MRP). The performance of the new model was confronted with the widely known many-objective algorithms MOEA/D and NSGA-III, in addition to the evolutionary models MEAMT and MEANDS proposed for discrete optimization problems that have also been evaluated in MKP and MRP. The results show that the MACO/NDS is competitive using instances with 4-6 objectives, proving to be a stable algorithm with good results in both problems.

Nowadays compilers include tens or hundreds of optimization passes, which makes it difficult to find sequences of optimizations that achieve compiled code more optimized than the one obtained using typical compiler options such as -O2 and -O3. The problem involves both the selection of the compiler passes to use and their ordering in the compilation pipeline. The improvement achieved by the use of custom phase orders for each function can be significant, and thus important to satisfy strict requirements such as the ones present in high-performance embedded computing systems. In this paper we present a new and fast iterative approach to the phase selection and ordering challenges resulting in compiled code with higher performance than the one achieved with the standard optimization levels of the LLVM compiler. The obtained performance improvements are comparable with the ones achieved by other iterative approaches while requiring considerably less time and resources. Our approach is based on sampling over a graph representing transitions between compiler passes. We performed a number of experiments targeting the LEON3 microarchitecture using the Clang/LLVM 3.7 compiler, considering 140 LLVM passes and a set of 42 representative signal and image processing C functions. An exhaustive cross-validation shows our new exploration method is able to achieve a geometric mean performance speedup of 1.28x over the best individually selected -OX flag when considering 100,000 iterations; versus geometric mean speedups from 1.16x to 1.25x obtained with state-of-the-art iterative methods not using the graph. From the set of exploration methods tested, our new method is the only one consistently finding compiler sequences that result in performance improvements when considering 100 or less exploration iterations. Specifically, it achieved geometric mean speedups of 1.08x and 1.16x for 10 and 100 iterations, respectively.

A large number of compiler optimizations are nowadays available to users. These optimizations interact with each other and with the input code in several and complex ways. The sequence of application of optimization passes can have a significant impact on the performance achieved. The effect of the optimizations is both platform and application dependent. The exhaustive exploration of all viable sequences of compiler optimizations for a given code fragment is not feasible. As this exploration is a complex and time-consuming task, several researchers have focused on Design Space Exploration (DSE) strategies both to select optimization sequences to improve the performance of each function of the application and to reduce the exploration time. In this article, we present a DSE scheme based on a clustering approach for grouping functions with similarities and exploration of a reduced search space resulting from the combination of optimizations previously suggested for the functions in each group. The identification of similarities between functions uses a data mining method that is applied to a symbolic code representation. The data mining process combines three algorithms to generate clusters: the Normalized Compression Distance, the Neighbor Joining, and a new ambiguity-based clustering algorithm. Our experiments for evaluating the effectiveness of the proposed approach address the exploration of optimization sequences in the context of the ReflectC compiler, considering 49 compilation passes while targeting a Xilinx MicroBlaze processor, and aiming at performance improvements for 51 functions and four applications. Experimental results reveal that the use of our clustering-based DSE approach achieves a significant reduction in the total exploration time of the search space (20× over a Genetic Algorithm approach) at the same time that considerable performance speedups (41% over the baseline) were obtained using the optimized codes. Additional experiments were performed considering the LLVM compiler, considering 124 compilation passes, and targeting a LEON3 processor. The results show that our approach achieved geometric mean speedups of 1.49 × , 1.32 × , and 1.24 × for the best 10, 20, and 30 functions, respectively, and a global improvement of 7% over the performance obtained when compiling with -O2.

This paper presents a new approach to efficiently search for suitable compiler pass sequences, a challenge known as phase ordering. Our approach relies on information about the relative positions of compiler passes in compiler pass sequences previously generated for a set of functions when compiling for a specific processor. We enhanced two iterative compiler pass exploration schemes, one relying on simple sequential compiler pass insertion and other implementing an auto-tuned simulated annealing process, with a data structure that holds information about the relative positions of compiler sequences; in order to reduce the set of compiler passes considered for insertion in a given position of a given candidate compiler pass sequence to include only the passes that have a higher probability of performing well on that relative position in the compiler sequence, speeding up the exploration time as a result. We tested our approach with two different compilers and two different targets; the ReflectC and the LLVM compilers, targeting a MicroBlaze processor and a LEON3 processor, respectively. The experimental results show that we can considerably reduce the number of algorithm iterations by a factor of up to more than an order of magnitude when targeting the MicroBlaze or the LEON3, while finding compiler sequences that result in binaries that when executed on the target processor/simulator are able to outperform (i.e. use less CPU cycles) all the standard optimization levels (i.e., we compare against the most performing optimization level flag on each kernel, e.g. -O1, -O2 or -O3 in the case of LLVM) by a geometric mean performance improvement of 1.23x and 1.20x when targeting the MicroBlaze processor, and 1.94x and 2.65x when targetting the LEON3 processor; for each of the two exploration algorithms and two kernel sets considered.

In this paper we present a clustering-based selection approach for reducing the number of compilation passes used in search space during the exploration of optimizations aiming at increasing the performance of a given function and/or code fragment. The basic idea is to identify similarities among functions and to use the passes previously explored each time a new function is being compiled. This subset of compiler optimizations is then used by a Design Space Exploration (DSE) process. The identification of similarities is obtained by a data mining method which is applied to a symbolic code representation that translates the main structures of the source code to a sequence of symbols based on transformation rules. Experiments were performed for evaluating the effectiveness of the proposed approach. The selection of compiler optimization sequences considering a set of 49 compilation passes and targeting a Xilinx MicroBlaze processor was performed aiming at latency improvements for 41 functions from Texas Instruments benchmarks. The results reveal that the passes selection based on our clustering method achieves a significant gain on execution time over the full search space still achieving important performance speedups.

Due to the large number of optimizations provided in modern compilers and to compiler optimization specific opportunities, a Design Space Exploration (DSE) is necessary to search for the best sequence of compiler optimizations for a given code fragment (e.g., function). As this exploration is a complex and time consuming task, in this paper we present DSE strategies to select optimization sequences to both improve the performance of each function and reduce the exploration time. The DSE is based on a clustering approach which groups functions with similarities and then explore the reduced search space provided by the optimizations previously suggested for the functions in each group. The identification of similarities between functions uses a data mining method which is applied to a symbolic code representation of the source code. The DSE process uses the reduced set identified by clustering in two ways: as the design space or as the initial configuration. In both ways, the adoption of a pre-selection based on clustering allows the use of simple and fast DSE algorithms. Our experiments for evaluating the effectiveness of the proposed approach address the exploration of compiler optimization sequences considering 49 compilation passes and targeting a Xilinx MicroBlaze processor, and were performed aiming performance improvements for 41 functions. Experimental results reveal that the use of our new clustering-based DSE approach achieved a significant reduction on the total exploration time of the search space (18x over a Genetic Algorithm approach for DSE) at the same time that important performance speedups (43% over the baseline) were obtained by the optimized codes.

A desirable characteristic in high-level synthesis (HLS) is fast search and analysis of implementation alternatives with low or none intervention. This process is known as Design Space Exploration (DSE) and it requires an efficient search method. The employment of intelligent techniques like evolutionary algorithms has been investigated as an alternative to DSE. They turn possible to reduce the search time through selection of higher potential regions of the solution space. We propose here the development of a DSE approach based on a multiobjective evolutionary algorithm (MOEA) and machine learning techniques. It must be employed to indicate the code transformations and architectural parameters adopted in design solution. Furthermore, DSE will use a high-level estimator model to evaluate candidate solutions. Such model must be able to provide a good estimation of energy consumption and execution time at early stages of design.

Cellular automata (CA) are able to perform complex computations through local interactions. The investigation of how CA computations are carried out can be made by the usage of CA rules to solve specific tasks. The well-known problem called density classification task (DCT) is investigated, with focus on its two-dimensional version. Evolutionary algorithms have been widely used in the search for DCT rules. A sample of lattices with Gaussian distribution is commonly used to evaluate rule quality. However, uniform lattices are easier to classify, allowing an initial selective pressure needed to start the convergence. A comparative evaluation of three adaptive strategies is presented here: they start using easy lattices to classify and as effective rules are being obtained the difficult level is progressively increased toward the target evaluation. Several experiments were performed to evaluate the strategies efficiency and new rules were found, which outperform the best ones published.