Above are papers related to the following paper: [Paper_ID] Paper #251756. 	[Title] Cellular Encoding Applied to Neurocontrol [Abstract] Neural networks are trained for balancing 1 and 2 poles attached to a cart on a fixed track. For one variant of the single pole system, only pole angle and cart position variables are supplied as inputs; the network must learn to compute velocities. All of the problems are solved using a fixed architecture and using a new version of cellular encoding that evolves an application specific architecture with real-valued weights. The learning times and generalization capabilities are compared for neural networks developed using both methods. After a post processing simplification, topologies produced by cellular encoding were very simple and could be analyzed. Architectures with no hidden units were produced for the single pole and the two pole problem when velocity information is supplied as an input. Moreover, these linear solutions display good generalization. For all the control problems, cellular encoding can automatically generate architectures whose complexity and structure reflect the features of the problem to solve. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1026. 	[Title] Genes, Phenes and the Baldwin Effect: Learning and Evolution in a Simulated Population [Abstract] The Baldwin Effect, first proposed in the late nineteenth century, suggests that the course of evolutionary change can be influenced by individually learned behavior. The existence of this effect is still a hotly debated topic. In this paper clear evidence is presented that learning-based plasticity at the phenotypic level can and does produce directed changes at the genotypic level. This research confirms earlier experimental work done by others, notably Hinton & Nowlan (1987). Further, the amount of plasticity of the learned behavior is shown to be crucial to the size of the Baldwin Effect: either too little or too much and the effect disappears or is significantly reduced. Finally, for learnable traits, the case is made that over many generations it will become easier for the population as a whole to learn these traits (i.e. the phenotypic plasticity of these traits will increase). In this gradual transition from a genetically driven population to one driven by learning, the importance of the Baldwin Effect decreases. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #593155. 	[Title] Cellular Encoding for Interactive Evolutionary Robotics [Abstract] Research in robotics programming is divided in two camps. The direct hand programmming approach uses an explicit model or a behavioral model ( subsumption architecture). The machine learning community uses neural network and/or genetic algorithm. We claim that hand programming and learning are complementary. The two approaches used together can be orders of magnitude more powerful than each approach taken separately. We propose a method to combine them both. It includes three concepts : syntactic constraints to restrict the search space, hand-made problem decomposition, hand given fitness. We use this method to solve a complex problem ( eight-legged locomotion). It needs 5000 less evaluations compared to when genetic algorithm are used alone. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #19045. 	[Title] AUTOMATED TOPOLOGY AND SIZING OF ANALOG CIRCUITS AUTOMATED DESIGN OF BOTH THE TOPOLOGY AND SIZING [Abstract] This paper describes an automated process for designing analog electrical circuits based on the principles of natural selection, sexual recombination, and developmental biology. The design process starts with the random creation of a large population of program trees composed of circuit-constructing functions. Each program tree specifies the steps by which a fully developed circuit is to be progressively developed from a common embryonic circuit appropriate for the type of circuit that the user wishes to design. Each fully developed circuit is translated into a netlist, simulated using a modified version of SPICE, and evaluated as to how well it satisfies the user's design requirements. The fitness measure is a user-written computer program that may incorporate any calculable characteristic or combination of characteristics of the circuit, including the circuit's behavior in the time domain, its behavior in the frequency domain, its power consumption, the number of components, cost of components, or surface area occupied by its components. The population of program trees is genetically bred over a series of many generations using genetic programming. Genetic programming is driven by a fitness measure and employs genetic operations such as Darwinian reproduction, sexual recombination (crossover), and occasional mutation to create offspring. This automated evolutionary process produces both the topology of the circuit and the numerical values for each component. This paper describes how genetic programming can evolve the circuit for a difficult-to-design low-pass filter. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #4584. 	[Title] References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages [Abstract] Miller, G. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. The Psychological Review, 63(2):81-97. Schmidhuber, J. (1990b). Towards compositional learning with dynamic neural networks. Technical Report FKI-129-90, Technische Universitat Munchen, Institut fu Informatik. Servan-Schreiber, D., Cleermans, A., and McClelland, J. (1988). Encoding sequential structure in simple recurrent networks. Technical Report CMU-CS-88-183, Carnegie Mellon University, Computer Science Department. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1131565. 	[Title] GENE REGULATION AND BIOLOGICAL DEVELOPMENT IN NEURAL NETWORKS: AN EXPLORATORY MODEL [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1128974. 	[Title] An Evolutionary Method to Find Good Building-Blocks for Architectures of Artificial Neural Networks [Abstract] This paper deals with the combination of Evolutionary Algorithms and Artificial Neural Networks (ANN). A new method is presented, to find good building-blocks for architectures of Artificial Neural Networks. The method is based on Cellular Encoding, a representation scheme by F. Gruau, and on Genetic Programming by J. Koza. First it will be shown that a modified Cellular Encoding technique is able to find good architectures even for non-boolean networks. With the help of a graph-database and a new graph-rewriting method, it is secondly possible to build architectures from modular structures. The information about building-blocks for architectures is obtained by statistically analyzing the data in the graph-database. Simulation results for two real world problems are given. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #195792. 	[Title] A Comparison between Cellular Encoding and Direct Encoding for Genetic Neural Networks [Abstract] This paper compares the efficiency of two encoding schemes for Artificial Neural Networks optimized by evolutionary algorithms. Direct Encoding encodes the weights for an a priori fixed neural network architecture. Cellular Encoding encodes both weights and the architecture of the neural network. In previous studies, Direct Encoding and Cellular Encoding have been used to create neural networks for balancing 1 and 2 poles attached to a cart on a fixed track. The poles are balanced by a controller that pushes the cart to the left or the right. In some cases velocity information about the pole and cart is provided as an input; in other cases the network must learn to balance a single pole without velocity information. A careful study of the behavior of these systems suggests that it is possible to balance a single pole with velocity information as an input and without learning to compute the velocity. A new fitness function is introduced that forces the neural network to compute the velocity. By using this new fitness function and tuning the syntactic constraints used with cellular encoding, we achieve a tenfold speedup over our previous study and solve a more difficult problem: balancing two poles when no information about the velocity is provided as input. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #94641. 	[Title] Complexity Compression and Evolution [Abstract] Compression of information is an important concept in the theory of learning. We argue for the hypothesis that there is an inherent compression pressure towards short, elegant and general solutions in a genetic programming system and other variable length evolutionary algorithms. This pressure becomes visible if the size or complexity of solutions are measured without non-effective code segments called introns. The built in parsimony pressure effects complex fitness functions, crossover probability, generality, maximum depth or length of solutions, explicit parsimony, granularity of fitness function, initialization depth or length, and modulariz-ation. Some of these effects are positive and some are negative. In this work we provide a basis for an analysis of these effects and suggestions to overcome the negative implications in order to obtain the balance needed for successful evolution. An empirical investigation that supports our hypothesis is also presented. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #650834. 	[Title] Neural Networks in an Artificial Life Perspective [Abstract] In the last few years several researchers within the Artificial Life and Mobile Robotics community used Artificial Neural Networks. Explicitly viewing Neural Networks in an Artificial Life perspective has a number of consequences that make research on what we will call Artificial Life Neural Networks ( ALNNs) rather different from traditional connectionist research. The aim of the paper is to make the differences between ALNNs and "classical" neural networks explicit. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1102550. 	[Title] Culling Teaching -1 Culling and Teaching in Neuro-evolution [Abstract] The evolving population of neural nets contains information not only in terms of genes, but also in the collection of behaviors of the population members. Such information can be thought of as a kind of culture of the population. Two ways of exploiting that culture are explored in this paper: (1) Culling overlarge litters: Generate a large number of offspring with different crossovers, quickly evaluate them by comparing their performance to the population, and throw away those that appear poor. (2) Teaching: Use backpropagation to train offspring toward the performance of the population. Both techniques result in faster, more effective neuro-evolution, and they can be effectively combined, as is demonstrated on the inverted pendulum problem. Additional methods of cultural exploitation are possible and will be studied in future work. These results suggest that cultural exploitation is a powerful idea that allows leveraging several aspects of the genetic algorithm. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #377303. 	[Title] Evolving Deterministic Finite Automata Using Cellular Encoding programming and cellular encoding. Programs are evolved that [Abstract] This paper presents a method for the initial singlestate zygote. The results evolution of deterministic finite Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #427606. 	[Title] Biological metaphors and the design of modular artificial neural networks Master's thesis of [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #62329. 	[Title] Automatic Definition of Modular Neural Networks [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #593210. 	[Title] Discovery of Symbolic, Neuro-Symbolic and Neural Networks with Parallel Distributed Genetic Programming [Abstract] Technical Report: CSRP-96-14 August 1996 Abstract Genetic Programming is a method of program discovery consisting of a special kind of genetic algorithm capable of operating on parse trees representing programs and an interpreter which can run the programs being optimised. This paper describes Parallel Distributed Genetic Programming (PDGP), a new form of genetic programming which is suitable for the development of parallel programs in which symbolic and neural processing elements can be combined a in free and natural way. PDGP is based on a graph-like representation for parallel programs which is manipulated by crossover and mutation operators which guarantee the syntactic correctness of the offspring. The paper describes these operators and reports some results obtained with the exclusive-or problem. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #682815. 	[Title] The Role of Development in Genetic Algorithms [Abstract] Technical Report Number CS94-394 Computer Science and Engineering, U.C.S.D. Abstract The developmental mechanisms transforming genotypic to phenotypic forms are typically omitted in formulations of genetic algorithms (GAs) in which these two representational spaces are identical. We argue that a careful analysis of developmental mechanisms is useful when understanding the success of several standard GA techniques, and can clarify the relationships between more recently proposed enhancements. We provide a framework which distinguishes between two developmental mechanisms | learning and maturation | while also showing several common effects on GA search. This framework is used to analyze how maturation and local search can change the dynamics of the GA. We observe that in some contexts, maturation and local search can be incorporated into the fitness evaluation, but illustrate reasons for considering them seperately. Further, we identify contexts in which maturation and local search can be distinguished from the fitness evaluation. Please summarise all the given information.

Paper #251756 has the title `Cellular Encoding Applied to Neurocontrol  
` and the abstract `Neural networks are trained for balancing 1 and 2 poles attached to a cart on a fixed track. For one variant of the single pole system, only pole angle and cart position variables are supplied as inputs; the network must learn to compute velocities. All of the problems are solved using a fixed architecture and using a new version of cellular encoding that evolves an application specific architecture with real-valued weights. The learning times and generalization capabilities are compared for neural networks developed using both methods. After a post processing simplification, topologies produced by cellular encoding were very simple and could be analyzed. Architectures with no hidden units were produced for the single pole and the two pole problem when velocity information is supplied as an input. Moreover, these linear solutions display good generalization. For all the control problems, cellular encoding can automatically generate architectures whose complexity and structure reflect the features of the problem to solve.
`.
Question: Which category should Paper #251756 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #251756 has the title `Cellular Encoding Applied to Neurocontrol  
` and the abstract `Neural networks are trained for balancing 1 and 2 poles attached to a cart on a fixed track. For one variant of the single pole system, only pole angle and cart position variables are supplied as inputs; the network must learn to compute velocities. All of the problems are solved using a fixed architecture and using a new version of cellular encoding that evolves an application specific architecture with real-valued weights. The learning times and generalization capabilities are compared for neural networks developed using both methods. After a post processing simplification, topologies produced by cellular encoding were very simple and could be analyzed. Architectures with no hidden units were produced for the single pole and the two pole problem when velocity information is supplied as an input. Moreover, these linear solutions display good generalization. For all the control problems, cellular encoding can automatically generate architectures whose complexity and structure reflect the features of the problem to solve.
`.
Question: Which category should Paper #251756 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Genetic_Algorithms