Fitness landscape of the cellular automata majority problem: View from the Olympus

In this paper we study cellular automata (CAs) that perform the computational Majority task. This task is a good example of what the phenomenon of emergence in complex systems is. We take an interest in the reasons that make this particular fitness landscape a difficult one. The first goal is to study the landscape as such, and thus it is ideally independent from the actual heuristics used to search the space. However, a second goal is to understand the features a good search technique for this particular problem space should possess. We statistically quantify in various ways the degree of difficulty of searching this landscape. Due to neutrality, investigations based on sampling techniques on the whole landscape are difficult to conduct. So, we go exploring the landscape from the top. Although it has been proved that no CA can perform the task perfectly, several efficient CAs for this task have been found. Exploiting similarities between these CAs and symmetries in the landscape, we define the Olympus landscape which is regarded as the ''heavenly home'' of the best local optima known (blok). Then we measure several properties of this subspace. Although it is easier to find relevant CAs in this subspace than in the overall landscape, there are structural reasons that prevent a searcher from finding overfitted CAs in the Olympus. Finally, we study dynamics and performance of genetic algorithms on the Olympus in order to confirm our analysis and to find efficient CAs for the Majority problem with low computational cost

Evolutionary Approaches to Optimization Problems in Chimera Topologies

Chimera graphs define the topology of one of the first commercially available quantum computers. A variety of optimization problems have been mapped to this topology to evaluate the behavior of quantum enhanced optimization heuristics in relation to other optimizers, being able to efficiently solve problems classically to use them as benchmarks for quantum machines. In this paper we investigate for the first time the use of Evolutionary Algorithms (EAs) on Ising spin glass instances defined on the Chimera topology. Three genetic algorithms (GAs) and three estimation of distribution algorithms (EDAs) are evaluated over $1000$ hard instances of the Ising spin glass constructed from Sidon sets. We focus on determining whether the information about the topology of the graph can be used to improve the results of EAs and on identifying the characteristics of the Ising instances that influence the success rate of GAs and EDAs.Comment: 8 pages, 5 figures, 3 table

Efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena

Structural learning of Bayesian Networks (BNs) is a NP-hard problem, which is further complicated by many theoretical issues, such as the I-equivalence among different structures. In this work, we focus on a specific subclass of BNs, named Suppes-Bayes Causal Networks (SBCNs), which include specific structural constraints based on Suppes' probabilistic causation to efficiently model cumulative phenomena. Here we compare the performance, via extensive simulations, of various state-of-the-art search strategies, such as local search techniques and Genetic Algorithms, as well as of distinct regularization methods. The assessment is performed on a large number of simulated datasets from topologies with distinct levels of complexity, various sample size and different rates of errors in the data. Among the main results, we show that the introduction of Suppes' constraints dramatically improve the inference accuracy, by reducing the solution space and providing a temporal ordering on the variables. We also report on trade-offs among different search techniques that can be efficiently employed in distinct experimental settings. This manuscript is an extended version of the paper "Structural Learning of Probabilistic Graphical Models of Cumulative Phenomena" presented at the 2018 International Conference on Computational Science

Generalized charge sensitivity analysis

Charge sensitivity analysis was originally introduced in the trivial-atom resolution. Here, we extend this resolution into force-field atoms. The AMBERff99 force-field resolution was employed. The effective elec- tronegativities and hardnesses were derived for five dif- ferent population analyses (Mulliken, Hirschfeld, AIM, NPA and Voronoi charges) by applying evolutionary algorithms

Novel integrated computational approach for designing Fe-Ni-based alloys

A novel computational approach for designing high temperature alloys is proposed; the approach incorporates properties prediction and optimisation. As for properties prediction, microstructural parameters, such as the volume fraction and precipitate size after heat treatment (replicating the service conditions of automobile sealing parts) were thermokinetically calculated by adopting phase transformation software. The associated yield stress was then predicted using classical strengthening theories: solid solution, grain boundary and precipitation strengthenings. These calculations were integrated with a genetic algorithm (GA) for searching the optimal chemical compositions considering not only the strength after a long time heat treatment but also cost and producibility constraints. The calculation parameters for the GA, such as population size and mutation ratio, were also considered. The alloy designed by the computer-aided approach described above was produced and validated. The designed alloy (Fe-opt) whose composition is Fe-33Ni-15.5Cr-1.6Al-0.3Nb-2.8Ti-3.7W-0.9Co-0.01C are proved to have the high strength after a long time high temperature exposure due to finely dispersed precipitates, $\gamma^{\prime}$, although the strength is not as high as expected from the calculation. The microstructure analysis suggests that W in the designed alloy has a negative influence on the mechanical properties of the alloy by forming coarse Laves phases on the grain boundaries. Therefore, the alloy sheet, with the same composition of Fe-opt but without W (Fe-opt2) was prepared and examined. Fe-opt2 has higher strength than Fe-opt and than more expensive Ni-based superalloys, such as Alloy 718Plus. The integrated approach conducted in this study has successfully provided an efficient and effective alloy design methodology. This approach can be widely adopted for use in many fields beyond high-temperature alloys by adopting suitable thermokinetic databases and strengthening modelling approaches.Nippon stee

Generating Interpretable Fuzzy Controllers using Particle Swarm Optimization and Genetic Programming

Autonomously training interpretable control strategies, called policies, using pre-existing plant trajectory data is of great interest in industrial applications. Fuzzy controllers have been used in industry for decades as interpretable and efficient system controllers. In this study, we introduce a fuzzy genetic programming (GP) approach called fuzzy GP reinforcement learning (FGPRL) that can select the relevant state features, determine the size of the required fuzzy rule set, and automatically adjust all the controller parameters simultaneously. Each GP individual's fitness is computed using model-based batch reinforcement learning (RL), which first trains a model using available system samples and subsequently performs Monte Carlo rollouts to predict each policy candidate's performance. We compare FGPRL to an extended version of a related method called fuzzy particle swarm reinforcement learning (FPSRL), which uses swarm intelligence to tune the fuzzy policy parameters. Experiments using an industrial benchmark show that FGPRL is able to autonomously learn interpretable fuzzy policies with high control performance.Comment: Accepted at Genetic and Evolutionary Computation Conference 2018 (GECCO '18