Robustness and evolvability are essential properties to the evolution of biological networks. To determine if a biological network is robust and/or evolvable, it is required to compare its functions before and after mutations. However, this sometimes takes a high computational cost as the network size grows. Here, we develop a predictive method to estimate the robustness and evolvability of biological networks without an explicit comparison of functions. We measure antifragility in Boolean network models of biological systems and use this as the predictor. Antifragility occurs when a system benefits from external perturbations. By means of the differences of antifragility between the original and mutated biological networks, we train a convolutional neural network (CNN) and test it to classify the properties of robustness and evolvability. We found that our CNN model successfully classified the properties. Thus, we conclude that our antifragility measure can be used as a predictor of the robustness and evolvability of biological networks.

Boolean networks are important models of biochemical systems, located at the high end of the abstraction spectrum. A number of Boolean gene networks have been inferred following essentially the same method. Such a method first considers experimental data for a typically underdetermined “regulation” graph. Next, Boolean networks are inferred by using biological constraints to narrow the search space, such as a desired set of (fixed-point or cyclic) attractors. We describe Griffin, a computer tool enhancing this method. Griffin incorporates a number of well-established algorithms, such as Dubrova and Teslenko's algorithm for finding attractors in synchronous Boolean networks. In addition, a formal definition of regulation allows Griffin to employ “symbolic” techniques, able to represent both large sets of network states and Boolean constraints. We observe that when the set of attractors is required to be an exact set, prohibiting additional attractors, a naive Boolean coding of this constraint may be unfeasible. Such cases may be intractable even with symbolic methods, as the number of Boolean constraints may be astronomically large. To overcome this problem, we employ an Artificial Intelligence technique known as “clause learning” considerably increasing Griffin's scalability. Without clause learning only toy examples prohibiting additional attractors are solvable: only one out of seven queries reported here is answered. With clause learning, by contrast, all seven queries are answered. We illustrate Griffin with three case studies drawn from the Arabidopsis thaliana literature. Griffin is available at: http://turing.iimas.unam.mx/griffin.

In this work a new procedure is presented for the simultaneous stabilization of linear time invariant SISO plants; evolutionary strategies and substitutions by polynomials and rational functions are used. The algorithm presented requires minimal computational resources for its execution. In addition, it is possible to work with some types of plants that could not be simultaneously stabilized until now, and there are many more of these than is described in the literature. The restrictions are minimal and the method can be extended with little difficulty to MIMO plants. Moreover, a mechanism of substitutions by polynomials and rational function is presented, and this mechanism preserves simultaneously stabilizable plants and their controllers. Several examples are given, showing the advantages of these procedures.

Hidden Markov models (HMMs) have found myriad uses. When employed in robotics, however, a conspicuous difference compared with finite-state machine emerges: HMMs lack a direct way of incorporating inputs. Although a sequence of observations can be viewed as input, we argue that it is desirable to extend HMMs with inputs in the vein of finite-state machines. As robot controllers, HMMs exhibit another salient feature: such models primarily focus on capturing uncertainty and stochasticity in the operational environment while maintaining deterministic actions based on the robot’s belief state. We propose extendin HMMs in this other direction as well, embracing uncertainty also in the reactive behavior of the robot, resulting in stochastic behavior, thus making the robot act differently under the same conditions. We argue that this produces rich information, often sufficient for effective navigation without explicit maps. In addition, this approach significantly reduces the computational burden on the agent and simultaneously mitigates undesirable behaviors arising from deterministic policies under uncertainty of environmental perception. An input output extended HMM (IOEHMM) can be regarded as a Moore machine with stochastic inputs and outputs. We build IOEHMM robot behaviors using a genetic algorithm to find the best models and compare this approach with robot behavior implemented with a Markov decision process (MDP), whose parameters are also found by a genetic algorithm. Our method outperforms MDPs in cases where obstacles are large or the robot is used for exploration.

This paper introduces Spectral Fault Receptive Fields (SFRFs), a biologically inspired technique for degradation state assessment in bearing fault diagnosis and remaining useful life (RUL) estimation. Drawing on the center-surround organization of retinal ganglion cell receptive fields, we propose a frequency-domain feature extraction algorithm that enhances the detection of fault signatures in vibration signals. SFRFs are designed as antagonistic spectral filters centered on characteristic fault frequencies, with inhibitory surrounds that enable robust characterization of incipient faults under variable operating conditions. A multi-objective evolutionary optimization strategy based on NSGA-II algorithm is employed to tune the receptive field parameters by simultaneously minimizing RUL prediction error, maximizing feature monotonicity, and promoting smooth degradation trajectories. The method is demonstrated on the XJTU-SY bearing run-to-failure dataset, confirming its suitability for constructing condition indicators in health monitoring applications. Key contributions include: (i) the introduction of SFRFs, inspired by the biology of vision in the primate retina; (ii) an evolutionary optimization framework guided by condition monitoring and prognosis criteria; and (iii) experimental evidence supporting the detection of early-stage faults and their precursors. Furthermore, we confirm that our diagnosis-informed spectral representation achieves accurate RUL prediction using a bagging regressor. The results highlight the interpretability and principled design of SFRFs, bridging signal processing, biological sensing principles, and data-driven prognostics in rotating machinery.