A study of the Lamarckian evolution of recurrent neural networks

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Adding learning to cellular genetic algorithms for training recurrent neural networks

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Impact of alife simulation of Darwinian and Lamarckian evolutionary theories

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementUntil nowadays, the scientific community firmly rejected the Theory of Inheritance of Acquired Characteristics, a theory mostly associated with the name of Jean-Baptiste Lamarck (1774-1829). Though largely dismissed when applied to biological organisms, this theory found its place in a young discipline called Artificial Life. Based on the two abstract models of Darwinian and Lamarckian evolutionary theories built using neural networks and genetic algorithms, this research aims to present a notion of the potential impact of implementation of Lamarckian knowledge inheritance across disciplines. In order to obtain our results, we conducted a focus group discussion between experts in biology, computer science and philosophy, and used their opinions as qualitative data in our research. As a result of completing the above procedure, we have found some implications of such implementation in each mentioned discipline. In synthetic biology, this means that we would engineer organisms precisely up to our specific needs. At the moment, we can think of better drugs, greener fuels and dramatic changes in chemical industry. In computer science, Lamarckian evolutionary algorithms have been used for quite some years, and quite successfully. However, their application in strong ALife can only be approximated based on the existing roadmaps of futurists. In philosophy, creating artificial life seems consistent with nature and even God, if there is one. At the same time, this implementation may contradict the concept of free will, which is defined as the capacity for an agent to make choices in which the outcome has not been determined by past events. This study has certain limitations, which means that larger focus group and more prepared participants would provide more precise results

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

Memetic cooperative coevolution of Elman recurrent neural networks

Cooperative coevolution decomposes an optimi- sation problem into subcomponents and collectively solves them using evolutionary algorithms. Memetic algorithms provides enhancement to evolutionary algorithms with local search. Recently, the incorporation of local search into a memetic cooperative coevolution method has shown to be efficient for training feedforward networks on pattern classification problems. This paper applies the memetic cooperative coevolution method for training recurrent neural networks on grammatical inference problems. The results show that the proposed method achieves better performance in terms of optimisation time and robustness

Connectionist Theory Refinement: Genetically Searching the Space of Network Topologies

An algorithm that learns from a set of examples should ideally be able to exploit the available resources of (a) abundant computing power and (b) domain-specific knowledge to improve its ability to generalize. Connectionist theory-refinement systems, which use background knowledge to select a neural network's topology and initial weights, have proven to be effective at exploiting domain-specific knowledge; however, most do not exploit available computing power. This weakness occurs because they lack the ability to refine the topology of the neural networks they produce, thereby limiting generalization, especially when given impoverished domain theories. We present the REGENT algorithm which uses (a) domain-specific knowledge to help create an initial population of knowledge-based neural networks and (b) genetic operators of crossover and mutation (specifically designed for knowledge-based networks) to continually search for better network topologies. Experiments on three real-world domains indicate that our new algorithm is able to significantly increase generalization compared to a standard connectionist theory-refinement system, as well as our previous algorithm for growing knowledge-based networks.Comment: See http://www.jair.org/ for any accompanying file