San Francisco – March Gene I. Sher. The Figures/Images in this slide presentation are taken from: ”Handbook of Neuroevolution Through Erlang” . Handbook of Neuroevolution Through Erlang presents both the theory behind, and Neuroevolution: Taking the First Step. Front Matter. Pages PDF. [PDF] DOWNLOAD Handbook of Neuroevolution Through Erlang by Gene I. Sher [PDF] DOWNLOAD Handbook of Neuroevolution Through.
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The 15 Invaluable Laws Of Growth by John Maxwell Instructor Notes average person? We are a product Thinking For A Chang. Request PDF on ResearchGate | Handbook of Neuroevolution Through Erlang | Handbook of Neuroevolution Through Erlang presents both the theory behind. Discuss the first of such Erlang implemented, general topology and parameter .. Incorporating Modularity. ○. Handbook of Neuroevolution Through Erlang.
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No notes for slide. Book details Author: Gene I. Sher Pages: Springer Language: English ISBN Description this book Please continue to the next pagenone https: If you want to download this book, click link in the last page 5. Handbook of Neuroevolution Through Erlang.
Authors view affiliations Gene I. Front Matter Pages i-xx. Pages Front Matter Pages Introduction to Neural Networks.
Introduction to Evolutionary Computation. Introduction to Neuroevolutionary Methods. Developing a Feed Forward Neural Network. Developing a Simple Neuroevolutionary Platform. Table 1 Comparative Characteristics of Neuroevolutionary Methods Sequence of modification of Chromosome Method Evolution method parameters and topology encoding technique ENS3 Parallel Direct Evolutionary algorithm NEAT Separate Direct Genetic algorithm EANT Separate Hybrid Evolutionary strategies DXNN Separate Direct and indirect Memetic algorithm CE Parallel Indirect Genetic programming Based on the above analysis of methods, one can make the following conclusions: most methods fail to modify the activation function type and its parameters and, at this, impose constraints on the neural network structure; the evolution in many methods runs exceptionally in a way of complexifying in some cases — sequentially simplifying the structure of an individual; some methods take a supervised learning approach, which requires the availability of representative case-based samples and additional constraints on the neural network structure.
Therefore, none of the existing methods combines such properties as absence of constraints on the individual to be optimized, dynamic nature of evolution, and modification of the most of all the allowable parameters of a neural network. In this connection, it seems actual to develop a novel neuroevolutionary method , which is free from the above-mentioned constrained and intended not only for adjusting the neuron weights and modifying the topology, but also for adjusting the threshold coefficients, type and parameters of activation functions.
The method proposed in the article is distinguished by an ingenious collection of properties, the main of which are: structural adaptivity and low connectivity of individuals, dynamic nature of evolution, as well as possibility of hybridization.
A compact form of representation opens an opportunity to operate the neural network topology with an extra-large number of neurons. For input neurons, this list is empty. Storage and recalculation of the parameters INi and OUTi, as well as indexing of the neural network nodes, encourage the solution of such problems as competition of representations and unprotectedness of innovations, which are typical for the direct encoding scheme: the new nodes added to the neural network by the mutation and crossing-over operators have larger indices than those formed in previous epochs of neuroevolution.
The availability of information about the shortest path to the input and output nodes prevents the crossing of the neural network sections that carry different functional loads. Mutation in the neural network sections changed during previous epochs is less probable than that in the unevolved sections. In particular, the indexing of nodes reduces the risk of removal of new elements from the population, which solves the problem of unprotected innovations.
It is the aforesaid universal representation that allows encoding of neural networks of any structure and size. The proposed method utilizes two pools containing, respectively, input parameters and the variety of possible neuron activation functions.
Based on the pool of input parameters constituting the majority of potential inputs of the neural network, the input vectors of individuals are formed in the evolution process. This pool is intended for optimizing the neural network efficiency, with the quality of network inference being maintained. The pool of activation functions contains the functions such as sigmoid function, Gaussian function, modified hypertangent, etc.
The pool is required for adjusting the parameters of each neuron and, accordingly, increasing the accuracy of neural network inference. Along with the encoding method, the selection of genetic operators and the type of fitness function have a direct impact on the neuroevolution efficiency.
This method makes it possible to explore the search space in full, avoid local extremes at the genetic search stage, and efficiently use "good" solutions to be found, i. The inverted pendulum has a center of mass above its support point and is positioned at the end of a rigid pole whose support point is attached to a cart. At the initial moment of time, the pendulum is deflected through the selected angle from the equilibrium position.
The task consists in setting the pendulum to the steady state by moving the cart with some force applied. In doing so, it is advised to avoid the ends of the track section. Three variants of the balancing system have been considered. Classical task on inverted pendulum balancing.
The inverted pendulum balancing task is considered to be solved successfully, if the neural network managed to hold the pendulum for 30 minutes, not moving the cart out of the preset interval.
Task on double inverted pendulum balancing. The pendulum poles have a common support point on the cart moving along the straight line. The task on two inverted pendulums balancing is considered to be solved successfully, if the neural network managed to hold both the pendulums for 30 minutes, not moving the cart out of the preset interval.
Task on inverted pendulum balancing on plane. The cart with its attached inverted pendulum moves along the straight line, but in a two-dimensional space.
The task on pendulum balancing on a plane is considered to be solved successfully, if the neural network managed to hold the pendulum for 30 minutes, not moving the cart out of the preset rectangular area.
To verify the efficiency of neuroevolutionary methods, the XOR function implementation task has been chosen for the construction of a classifier of linearly unseparable patterns, which is a particular case of the task on classification of unit hypercube points.
Although the implementation of logical functions, in itself, by use of a neural network is considered trivial, it was used for testing for two reasons. First, testing for optimization of parameters: weights, type of activation functions, and threshold values. Implementation of the XOR function clearly demonstrates the accuracy of adjustment of weights and the optimality of choosing the threshold function as an activation functions.
Second, testing for relevance of the neural network structure to be generated. For successful solution of the XOR task, a multilayer perceptron should have two inputs, a hidden layer of four neurons, and one output. A single hidden layer should be formed in the neural network, which consists of two neurons with threshold activation functions.
A learning set based on the XOR function truth table has been formed to solve this task by using neural networks to be generated by the neuroevolutionary methods.
Tasks on recovery of damaged data noisy signals, damaged images. They are exponential, due to poor formalization, when testing the neuroevolutionary methods. After that, a recovered pattern is formed at the output of the neural network. The images U and V are represented in the form of three color matrices corresponding to the RGB model.
To attain correct comparison of efficient of the methods, the task solution results have been averaged as per starts. Evaluation has been made on the basis of the indirect measures of solution search efficiency by use of the neuroevolutionary methods, namely: the number of evolution epochs needed to solve the assigned tasks, and the number of failed neuroevolution starts whose results demonstrated that neither optimal nor close to optimal solution was formed .
The number of evolution epochs grows as the tasks become more complicated.
Nevertheless, the results meeting the specified criteria were formed for all the tasks of the benchmarks series. The proposed method KHO demonstrates the best or close to the best results and outperforms most previous methods as far as the number of epochs and the number of failed starts are concerned. Thus, the data obtained in process of the experimental investigation of efficiency of the method based on the neuroevolutionary benchmark tasks testifies that the method is efficient.
In particular, for complex tasks, the most of which are NP-complete, the neuroevolutionary methods demonstrate splendid results. This is confirmed by the experimental investigation of efficiency of neuroevolutionary methods based on most practice-oriented benchmark tasks.
The proposed method KHO demonstrates the best results when solving the tasks based on inverted pendulum balancing and image recovery, or close to the best results for the tasks based on double inverted pendulum balancing, inverted pendulum balancing on a plane, and implementation of the logical XOR function. The analysis of the experiment results allows making the conclusion that it is expedient to use the method in practice. It is accepted that the neuroevolutionary approach, in spite of its growing popularity in implementing the decision support system modules, is considered as an alternative trend in the decision making theory.
The article shows that the neuroevolutionary methods demonstrate high quality values when solving the tasks in diverse domains, including the tasks on recovery of damaged data, control of dynamic objects, and classification of linearly inseparable patterns.
From a scientific perspective, discovering how the brain thinks and makes decision are a major undertaking in the history of humankind. Bioinformatics provides computational and experimental tools to study the biological patterns, structures, and functions.
How data was transcribed into information? How information was translated into knowledge? These are fundamental questions that require further investigations. Data mining is an active field that exams the process of extracting hidden patterns from data. Knowledge discovery is a growing field to exam the process of converting the information into knowledge .
Recent advances in experimentation such as patch clamp recording, voltage- and ion-specific dyes, and confocal microscopy are providing data to facilitate further theoretical development for addressing fundamental issues that range from the sub- cellular to cell-ensemble to whole-system levels.
We must synthesize information and mechanisms across these different levels for thorough understanding from molecule to ecosystem. This is perhaps the fundamental challenge facing mathematical and theoretical biology.
Models of neural interactions lead to many interesting mathematical questions for which appropriate tools must be developed.
Typically, networks are modeled by possibly stochastic systems of differential equations. In some simplified limits, these become nonlinear integro-differential equations. The question now becomes one of proving or otherwise demonstrating that the simplified models have the desired behavior.
Furthermore, one must characterize this behavior as parameters in the model vary i. Another important point that mathematicians must address is the extraction of the underlying geometric and analytic ideas from detailed biophysical models and simulations .