Simulation of Multiple Cell Population Dynamics Using a 3-D Cellular Automata Model for Tissue Growth

In this paper, the authors describe a computational model for the growth of multicellular tissues using a discrete approach based on cellular automata to simulate the tissue growth rates and population dynamics of multiple populations of proliferating and migrating cells. Each population of cells has its own division, motion, collision, and aggregation characteristics. These random dynamic processes can be modeled by appropriately choosing the governing rules of the state transitions of each computational site. This extended model contains a number of system parameters that allow their effects on the volume coverage, the overall tissue growth rate, and some other aspects of cell behavior like the average speed of locomotion to be explored. These discrete systems provide an alternative approach to continuous models for the purpose of describing the temporal dynamics of complex systems.

Robust Design of Power Distribution Systems Using an Enhanced Multi-Objective Genetic Algorithm

The growing importance of electric distribution systems justifies new investments in their expansion and evolution. It is well known in the literature that optimization techniques can provide better allocation of the financial resources available for such a task, reducing total installation costs and power losses. In this work, the NSGA-II algorithm is used for obtaining a set of efficient solutions with regard to three objective functions, that is cost, reliability, and robustness. Initially, a most likely load scenario is considered for simulation. Next, the performances of the solutions achieved by the NSGA-II are evaluated under different load scenarios, which are generated by means of Monte Carlo Simulations. A Multi-objective Sensitivity Analysis is performed for selecting the most robust solutions. Finally, those solutions are submitted to a local search algorithm to estimate a Pareto set composed of just robust solutions only.

Feature Selection for Bankruptcy Prediction

In this work a Multi-Objective Evolutionary Algorithm (MOEA) was applied for feature selection in the problem of bankruptcy prediction. This algorithm maximizes the accuracy of the classifier while keeping the number of features low. A two-objective problem, that is minimization of the number of features and accuracy maximization, was fully analyzed using the Logistic Regression (LR) and Support Vector Machines (SVM) classifiers. Simultaneously, the parameters required by both classifiers were also optimized, and the validity of the methodology proposed was tested using a database containing financial statements of 1200 medium sized private French companies. Based on extensive tests, it is shown that MOEA is an efficient feature selection approach. Best results were obtained when both the accuracy and the classifiers parameters are optimized. The proposed method can provide useful information for decision makers in characterizing the financial health of a company.

Conspecific Emotional Cooperation Biases Population Dynamics

In this paper, the authors evaluate the benefit of emotions in population dynamics and evolution. The authors enhance cellular automata (CA) simulating the interactions of competing populations with emotionally inspired rules in communication, interpretation, and action. While CAs have been investigated in studies of population dynamics due to their ability to capture spatial interactions, emotion-like interactions have yet to be considered. Our cellular stochastic system describes interacting foxes that feed on rabbits that feed on carrots. Emotions enable foxes and rabbits to improve their decisions and share their experiences with neighboring conspecifics. To improve the system’s biological relevance, it includes inter-species disease transmission, and emotions encode data pertaining to both survival and epidemic reduction. Results indicate that emotions increase adaptability, help control disease, and improve survival for the species that utilizes them. Simulations support the hypothesis that the acquisition of emotion may be an evolutionary result of competitive species interactions.

Adaptability and Homeostasis in the Game of Life interacting with the evolved Cellular Automata

In this paper, the authors study the emergence of homeostasis in a two-layer system of the Game of Life, in which the Game of Life in the first layer couples with another system of cellular automata in the second layer. Homeostasis is defined as a space-time dynamic that regulates the number of cells in state-1 in the Game of Life layer. A genetic algorithm is used to evolve the rules of the second layer to control the pattern of the Game of Life. The authors found that two antagonistic attractors control the numbers of cells in state-1 in the first layer. The homeostasis sustained by these attractors is compared with the homeostatic dynamics observed in Daisy World.

A New Differential Evolution Based Metaheuristic for Discrete Optimization

The Differential Evolution (DE) algorithm is an important and powerful evolutionary optimizer in the context of continuous numerical optimization. Recently, some authors have proposed adaptations of its differential mutation mechanism to deal with combinatorial optimization, in particular permutation-based integer combinatorial problems. In this paper, the authors propose a novel and general DE-based metaheuristic that preserves its interesting search mechanism for discrete domains by defining the difference between two candidate solutions as a list of movements in the search space. In this way, the authors produce a more meaningful and general differential mutation for the context of combinatorial optimization problems. The movements in the list can then be applied to other candidate solutions in the population as required by the differential mutation operator. This paper presents results on instances of the Travelling Salesman Problem (TSP) and the N-Queen Problem (NQP) that suggest the adequacy of the proposed approach for adapting the differential mutation to discrete optimization.

Quantum Automata with Open Time Evolution

In this paper, a model for finite automaton with an open quantum evolution is introduced, and its basic properties are studied. It is shown that the (fuzzy) languages accepted by open evolution quantum automata obey various closure properties. More importantly, it is shown that major other models of finite automata, including probabilistic, measure once quantum, measure many quantum, and Latvian quantum automata can be simulated by the open quantum evolution automata without increasing the number of the states.

Wave Propagation in Filamental Cellular Automata

Motivated by questions in biology and distributed computing, the authors investigate the behaviour of particular cellular automata, modelled as one-dimensional arrays of identical finite automata. They investigate what kinds of self-stabilising cooperative behaviour may be induced in terms of waves of cellular state changes along a filament of cells. The authors report the minimum requirements, in terms of numbers of states and the range of communication between automata, for this behaviour to be observed in individual filaments. They also discover that populations of growing filaments may have useful features not possessed by individual filaments, and they report the results of numerical simulations.

Cognitively Inspired Neural Network for Recognition of Situations

The authors present a cognitively inspired mathematical learning framework called Neural Modeling Fields (NMF). They apply it to learning and recognition of situations composed of objects. NMF successfully overcomes the combinatorial complexity of associating subsets of objects with situations and demonstrates fast and reliable convergence. The implications of the current results for building multi-layered intelligent systems are also discussed.

Analysis of a Step-Based Watershed Algorithm Using CUDA

This paper proposes and develops a parallel algorithm for the watershed transform, with application on graphics hardware. The existing proposals are discussed and its aspects briefly analysed. The algorithm is proposed as a procedure of four steps, where each step performs a task using different approaches inspired by existing techniques. The algorithm is implemented using the CUDA libraries and its performance is measured on the GPU and compared to a sequential algorithm running on the CPU, achieving an average speed of twice the execution time of the sequential approach. This work improves on previous results of hybrid approaches and parallel algorithms with many steps of synchronisation and iterations between CPU and GPU.