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.
However, although Neuroevolution has been widely studied and successfully applied in several domains, the field still lacks a reference book for newcomers. Handbook of Neuroevolution Through Erlang is not a general introduction to the field, instead it guides the reader through the development of a Neuroevolutionary system using Erlang, a functional and concurrency oriented programming language. Following a hands-on approach, Sher shows how to implement step-by-step all the components and features of an advanced Neuroevolutionary system. The book starts with a general introduction about the field of Neuroevolution and is then organized in six parts. The first part consists of four chapters Chapter 2—Chapter 5 and provides background for the reminder of the book; Chapter 2 introduces Artificial Neural Networks and both the supervised as well as the unsupervised learning algorithms used to train them; Chapter 3 introduces the basic elements of Evolutionary Computation and some of the major evolutionary algorithms; Chapter 4 introduces Neuroevolution discussing also some of the issues and challenges in the field; Chapter 5 briefly explains how the features of Erlang originally designed to develop telecommunication systems fit the needs of a Neuroevolutionary system. The second part of the book Chapter 6—Chapter 9 guides the readers through the first development steps of a Neuroevolutionary system using Erlang. The fifth part of the book illustrates possible applications for the Neuroevolutionary system developed: Chapter 18 describes the application of the system to an artificial life environment, while Chapter 19 shows a financial application.
The publisher makes no warranty, express or implied, with respect to the material contained herein. Gene addresses what is a fascinating problem: How can we simulate a biologi- cal system in a computer. Can we make a system that learns from its mistakes?
Gene chose to program his system in Erlang, which is where I come in. Erlang was designed over twenty five years ago to solve a completely different problem. What we were trying to do at the time and we are still trying, is to make a lan- guage for programming extremely large systems that never stop. Our application was telephony. We wanted to write the control software for a set of large telephone exchanges.
This software should in principle run forever. The telephone networks span the planet, and the control systems for these net- works were running before the invention of the computer. Any system like this must be able to tolerate both software and hardware fail- ures, and thus we built a programming language where failures were not a prob- lem. It would never be the case that the software in the system would be correct, right from the start, instead we would have to change the software many times during the life of the product.
And we would also have to make these changes without stopping the system. The view of the world that Erlang presents the programmer is intrinsically dis- tributed, intrinsically changing and capable of self-repair.
Neuroevolution was far from our thoughts. Twenty years later Gene comes along and discovers Erlang - to him, Erlang processes are neurons.
Well of course Erlang processes are not neurons, but they can easily be programmed to behave like neurons. So today we can run a few million processes per node, and a few dozen nodes per chip. Computer architectures have changed from the single core Von-Neumann machine, to the multicore processor, and the archi- tectures will change again.
Nobody actually knows how they are going to change, but my bet is that the change will be towards network-on-chip architectures. We can imagine large regular matrices of CPUs connected into a regular switching infrastructure. What will we do with such a monster computer and how are we going to pro- gram it? This book will tell you how.
Will it work in the near future or will this take hundreds of years? Nobody knows, but the journey will be interesting and we might build some other useful things on the way. Uncertainty of the mathematical description, the absence of a formal model and non-triviality are the properties of problems for which classical methods give inadequate results. In principle, such methods are not intended to solve problems of intellectual processing and analysis of data.
Neuro-fuzzy models are successfully used as their replacement. There is an extensive experience of their implementation and use in various subject areas, including the formulation of medical diagnoses, autopilot training, radar signal recognition, information filtering, monitoring and prevention of emergency situations, analysis of seismic activity, etc.
Neuroevolution is a dynamically developing computational intelligence discipline dealing with the study and development of hybrid methods for the design of neural networks by using evolutionary algorithms . The primary goal of integration of neuroevolutionary modules into the decision making support systems is that it assists a decision maker to plan activities according to varying environment conditions. First of all, such problems are characteristics of application domains not containing any functionally complete mathematical theories that describe the decision making objects and models .
As techniques of automatizing the decision making processes in such application domains, various neuroevolutionary methods are used with ever increasing frequency. This article presents a comparative analysis of the fundamental neuroevolutionary methods in Section 2 and, based on this analysis, suggests a novel method KHO that allows modifying the topology and the parameters of a neural network, not imposing any additional constraint on that neural network in Section 3. The results of solving the series of traditional benchmark tasks by using the analyzed neuroevolutionary methods and the proposed method are also presented in Section 4.
The aforesaid tasks are used in the neuroevolution field to make an indirect analysis of efficiency, performance, reliability and other parameters of the methods. The benchmark tasks serve as a criterion for selection of one or another method to be used in different practical application fields, and the successful passing of the series of benchmark tests is evidence of stable performance of the method.
Let us consider the main of them. Figure 1 Timeline of Development of Main Neuroevolutionary Methods CE Cellular Encoding — the indirect encoding method aimed at evolution of the sequence of using the rules that control the division of cells from which a neural network is produced . The CE method is intended for parallel modification of the topology by way of sequentially complexifying and setting weight of the neural network.
The CE method is primarily oriented to the construction of modular neural networks consisting of hierarchically linked subnetworks. It is also useful for formation of patterns and recursive structures.
An advantage of this method is the possibility of making changes in the neuron activation function. Also, the method makes it possible to produce a neural network of any configuration, without constraints on the number of neurons and the topology. D case, the individuals formed as a result of the use of genetic operators are guaranteed to be viable.
A disadvantage of the method is high resource intensity because each cell stores a copy of the grammar tree, as well as the markers and internal registers. Since the method implements the indirect encoding, it is characterized by low efficiency resulting from the necessity to carry out the grammar tree encoding-decoding operations.
DXNN Deus Ex Neural Network — the memetic algorithm based method for separate modification of the topology and weights of a neural network . Depending on its implementation, the DXNN method supports the direct and indirect chromosome encoding techniques. For evolution of the neural network topology, the method provides for a global search stage, while at a local search stage it optimizes the synaptic weights only.
The memetic approach implemented in the DXNN method has a number of advantages. The sequential modification of the topology and weights allows determining whether the given individual demonstrates low fitness due to an unsuccessfully formed topology or due to incorrectly selected weights.
Also, in most neuroevolutionary methods, the operators involved in changing the weight values are applied indiscriminately to all neurons of the neural network, thus making the probability of optimizing the new and the right neuron, very low. In fact, the memetic methods, especially DXNN, optimize the weights of recently modified neurons, not affecting the architecture already optimized during previous iterations.
At the same time, the DXNN method is characteristic of some disadvantages: the evolution follows the path of complexifying the topology and increasing the number of neurons because the method does not realize such mutation operators as removal of the link or removal of the neuron; and of all the neuron parameters, the method optimizes the weights only.
In this connection, the optimum solution time increases exponentially at a linear increase of task complexity. The CGE scheme is intended for separate evolution of both the structure and parameters of neural networks, and is characterized by possessing two important properties: completeness and closedness.
The CGE scheme defines a genome as a linear sequence of genes capable of taking one of the three different forms alleles : input, node or jumper. The input is the gene designating the input neuron. The node is the gene designating the neuron to which four parameters are related, namely: the weight, the current value of activation function, the GUID Global Unique IDentifier , and the number of input connections.
The Jumper is the synaptic connection gene that stores references to the two nodes connected by the synaptic connection, and the GUID of the neuron to which the jumper is connected.
This method of genome representation can be interpreted as a linear program encoding a prefix tree-based program if one assumes that all the inputs of the neural network and all jumper connections are terminals, and the neurons are functions. A tree-based program can be stored in an array linear genome where the tree structure topology of the neural network is implicitly coded in the ordering of the elements of the array.
Pertaining to the advantages of the EANT method is as follows: a compact encoding of the genome, an absence of the decoding phase, and, as a consequence, a high operating speed. It should be noted that it is expedient to use this method in the systems having a constraint on the task solution time.
A disadvantage of the EANT method is an absence of crossing-over operators and structural mutation operators for removal of neurons, and, as a consequence, restriction of the genetic search space. It is applied to neural networks of standard additive neurons with sigmoidal transfer functions and sets no constraints on the number of neurons and the topology of a network.
The method develops neural network topology and parameters like bias terms and weights simultaneously.
The method is based on a behavior-oriented approach to neural systems. The algorithm originally was designed to study the appearance of complex dynamics in artificial sensorimotor systems for autonomous robots and software agents. The method has advantages such as simplicity of implementation and good performance for small- and medium-sized neural networks. However, no crossing-over operator is implemented in the ENS3 method, which is considered to be a disadvantage, because when implemented properly, the crossing-over is capable of considerably reducing the evolution time and speeding up formation of an optimal individual.
The ENS3 method also allows no modification of the pattern of neuron activation functions. Besides, fixed probabilities of mutation for neurons and connections reduce the search space: many effective configurations of neural networks cannot be formed due to low probability of changes in respective nodes of neural networks of the current population.
NEAT Neuro-Evolution by Augmenting Topologies — the method intended for optimizing weights and sequentially complexifying the structure of a neural network .
The initial population is generated from fully-connected neural networks consisting of input and output layers, where the number of neurons is predetermined. The genome structure in this method is based on the list of synapses. Each synapse stores the indices of two neurons the signal source and receiver , the weight of the connection, an enable bit indicating whether the given synapse is active, and innovation number, which allows finding similar genes during crossing-over. The method uses the direct encoding scheme and implements two mutation operators for separate modification of the weights and the structure; with the probability of mutation being fixed for each weight.
The structural mutations increase the genome size owing to adding new genes, and add either the connection for two early unconnected neurons or the new neuron, whereas the existing connection is divided into two connections — the input and output of the new neuron. The replaceable connection is marked as inactive; the incoming connection weight is defined to be equal to one, and the outgoing connection weight is equated with the replaceable connection weight.
The crossing-over operator is based on biological concepts of homologous genes alleles and the synapsis process — alignment of homologous genes before crossing-over. The NEAT method uses innovation numbers — the historical markers associated with each gene for the purpose of tracking a chronology of changes to be made in that gene.
The historical markers are recomputed in the following manner: whenever a new gene appears, the global innovation number is incremented and assigned to that gene. The gene of one of the individuals formed for crossing-over of a pair with the innovation number differing from all innovation numbers of genes of the other individual is called the disjoint gene. The genes appearing in the given individual later than any of the other individual genes are called the excess genes.
The genes with the same innovation numbers get aligned and form a genome for the next generation by mixing appropriate genes in a random manner or averaging the weights of connections. At the crossing-over stage, the probability of reactivation is specified for nonactivated genes.
The NEAT method is effective with species within the population by way of separately computing the fitness of each species, which allows ensuring the genetic diversity. D introducing a metric measure into the space of genomes. The NEAT method has the advantages such as protecting topological innovations by historical markers and preserving a structural diversity in the population owing to specialization.
These two approaches make it possible to solve the problems of premature convergence and unprotectedness of innovations. Nevertheless, the evolution by way of sequential complexification, which has been implemented in this method, is the cause of such disadvantages as search space restriction and high resource intensity.
The results of the comparative analysis of neuroevolutionary method are specified in Table 1 below. 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. 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. Testing the Neuroevolutionary System. A Case Study.