Meta-Learning by the Baldwin Effect

The scope of the Baldwin effect was recently called into question by two papers that closely examined the seminal work of Hinton and Nowlan. To this date there has been no demonstration of its necessity in empirically challenging tasks. Here we show that the Baldwin effect is capable of evolving few-shot supervised and reinforcement learning mechanisms, by shaping the hyperparameters and the initial parameters of deep learning algorithms. Furthermore it can genetically accommodate strong learning biases on the same set of problems as a recent machine learning algorithm called MAML "Model Agnostic Meta-Learning" which uses second-order gradients instead of evolution to learn a set of reference parameters (initial weights) that can allow rapid adaptation to tasks sampled from a distribution. Whilst in simple cases MAML is more data efficient than the Baldwin effect, the Baldwin effect is more general in that it does not require gradients to be backpropagated to the reference parameters or hyperparameters, and permits effectively any number of gradient updates in the inner loop. The Baldwin effect learns strong learning dependent biases, rather than purely genetically accommodating fixed behaviours in a learning independent manner

Parameters Identification for a Composite Piezoelectric Actuator Dynamics

This work presents an approach for identifying the model of a composite piezoelectric (PZT) bimorph actuator dynamics, with the objective of creating a robust model that can be used under various operating conditions. This actuator exhibits nonlinear behavior that can be described using backlash and hysteresis. A linear dynamic model with a damping matrix that incorporates the Bouc–Wen hysteresis model and the backlash operators is developed. This work proposes identifying the actuator’s model parameters using the hybrid master-slave genetic algorithm neural network (HGANN). In this algorithm, the neural network exploits the ability of the genetic algorithm to search globally to optimize its structure, weights, biases and transfer functions to perform time series analysis efficiently. A total of nine datasets (cases) representing three different voltage amplitudes excited at three different frequencies are used to train and validate the model. Four cases are considered for training the NN architecture, connection weights, bias weights and learning rules. The remaining five cases are used to validate the model, which produced results that closely match the experimental ones. The analysis shows that damping parameters are inversely proportional to the excitation frequency. This indicates that the suggested hysteresis model is too general for the PZT model in this work. It also suggests that backlash appears only when dynamic forces become dominant

Lamarck's Revenge: Inheritance of Learned Traits Can Make Robot Evolution Better

Evolutionary robot systems offer two principal advantages: an advanced way of developing robots through evolutionary optimization and a special research platform to conduct what-if experiments regarding questions about evolution. Our study sits at the intersection of these. We investigate the question ``What if the 18th-century biologist Lamarck was not completely wrong and individual traits learned during a lifetime could be passed on to offspring through inheritance?'' We research this issue through simulations with an evolutionary robot framework where morphologies (bodies) and controllers (brains) of robots are evolvable and robots also can improve their controllers through learning during their lifetime. Within this framework, we compare a Lamarckian system, where learned bits of the brain are inheritable, with a Darwinian system, where they are not. Analyzing simulations based on these systems, we obtain new insights about Lamarckian evolution dynamics and the interaction between evolution and learning. Specifically, we show that Lamarckism amplifies the emergence of `morphological intelligence', the ability of a given robot body to acquire a good brain by learning, and identify the source of this success: `newborn' robots have a higher fitness because their inherited brains match their bodies better than those in a Darwinian system.Comment: preprint-nature scientific report. arXiv admin note: text overlap with arXiv:2303.1259

Aplicación de la Descomposición Empírica en Modo a la Predicción del Mercado Bursátil con los Modelos de ARIMA-ARCH y Redes Neuronales Artificiales Evolutivas

Tesis de Maestría donde se propone un modelo de Ensembles de Redes Neuronales Artificiales para predecir series de tiempo financiaeras de MéxicoEl mercado bursátil es un sistema dinámico que se caracteriza por su complejidad, volatilidad, no estacionariedad, irregularidad, pero sobre todo por las repentinas y pronunciadas caídas en los precios. Dadas estas características, y con el fin de contrarrestar las fluctuaciones aparentemente aleatorias, la inherente no linealidad en los datos financieros, y puesto que en muchos de los enfoques tradicionales que abordan la predicción del mercado bursátil en periodos de crisis, estos por lo regular no son capaces de capturar de manera fiable los rasgos distintivos del fenómeno. En esta investigación, se propone como primer paso, descomponer a los indicadores que representan al mercado accionario de los Estados Unidos y México en periodos de crisis, mediante la herramienta llamada Descomposición Empírica en Modos (DEM) que se encarga de descomponer la serie original de los índices accionarios en un número finito de descomposiciones llamadas Funciones de Modo Intrínseco (FMIs) y un elemento residual. A continuación, cada una de las FMIs y el residuo, son pronosticadas individualmente, utilizando por un lado, un modelo paramétrico (Autorregresivo Integrado de Media Móvil-Modelo de Volatilidad Condicional Heterocedástico (ARIMA-ARCH)) y por otro lado, por un modelo no paramétrico Redes Neuronales Artificiales (RNAs), este último es configurado por medio de un algoritmo evolutivo llamado Selección de Características de Programación Evolutiva de Redes Neuronales Artificiales (FS- EPNet). Posteriormente, se adquiere la predicción del modelo paramétrico, mediante la suma de las predicciones resultantes de cada FMI y del residuo, de igual forma se realiza el mismo procedimiento para obtener la predicción final del modelo no paramétrico. Finalmente, las predicciones de los modelos paramétrico y no paramétrico son combinadas mediante un promedio ponderado, para producir una combinación de pronósticos, estas predicciones a su vez son comparadas. Los resultados empíricos obtenidos demuestran que los modelos que colaboraron en conjunción con la técnica de descomposición de señales DEM, tienen una predicción más precisa de la crisis bursátil, a diferencia de los modelos que confeccionaron su pronóstico de manera aislada.COMECyT, CONACy