Above are papers related to the following paper: [Paper_ID] Paper #286513. 	[Title] Specialization under Social Conditions in Shared Environments [Abstract] Specialist and generalist behaviors in populations of artificial neural networks are studied. A genetic algorithm is used to simulate evolution processes, and thereby to develop neural network control systems that exhibit specialist or generalist behaviors according to the fitness formula. With evolvable fitness formulae the evaluation measure is let free to evolve, and we obtain a co-evolution of the expressed behavior and the individual evolvable fitness formula. The use of evolvable fitness formulae lets us work in a dynamic fitness landscape, opposed to most work, that traditionally applies to static fitness landscapes, only. The role of competition in specialization is studied by letting the individuals live under social conditions in the same, shared environment and directly compete with each other. We find, that competition can act to provide population diversification in populations of organisms with individual evolvable fitness formulae. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #35. 	[Title] 4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley. [Abstract] To implement a specific application, you should only have to change the file app.c. Section 2 describes the routines in app.c in detail. If you use additional variables for your specific problem, the easiest method of making them available to other program units is to declare them in sga.h and external.h. However, take care that you do not redeclare existing variables. Two example applications files are included in the SGA-C distribution. The file app1.c performs the simple example problem included with the Pascal version; finding the maximum of x 10 , where x is an integer interpretation of a chromosome. A slightly more complex application is include in app2.c. This application illustrates two features that have been added to SGA-C. The first of these is the ithruj2int function, which converts bits i through j in a chromosome to an integer. The second new feature is the utility pointer that is associated with each population member. The example application interprets each chromosome as a set of concatenated integers in binary form. The lengths of these integer fields is determined by the user-specified value of field size, which is read in by the function app data(). The field size must be less than the smallest of the chromosome length and the length of an unsigned integer. An integer array for storing the interpreted form of each chromosome is dynamically allocated and assigned to the chromosome's utility pointer in app malloc(). The ithruj2int routine (see utility.c) is used to translate each chromosome into its associated vector. The fitness for each chromosome is simply the sum of the squares of these integers. This example application will function for any chromosome length. SGA-C is intended to be a simple program for first-time GA experimentation. It is not intended to be definitive in terms of its efficiency or the grace of its implementation. The authors are interested in the comments, criticisms, and bug reports from SGA-C users, so that the code can be refined for easier use in subsequent versions. Please email your comments to rob@galab2.mh.ua.edu, or write to TCGA: The authors gratefully acknowledge support provided by NASA under Grant NGT-50224 and support provided by the National Science Foundation under Grant CTS-8451610. We also thank Hillol Kargupta for donating his tournament selection implementation. Booker, L. B. (1982). Intelligent behavior as an adaptation to the task environment (Doctoral dissertation, Technical Report No. 243. Ann Arbor: University of Michigan, Logic of Computers Group). Dissertations Abstracts International, 43(2), 469B. (University Microfilms No. 8214966) Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1131557. 	[Title] Specialization in Populations of Artificial Neural Networks [Abstract] Specialization in populations of artificial neural networks is studied. Organisms with both fixed and evolvable fitness formulae are placed in isolated and shared environments, and the emerged behaviors are compared. An evolvable fitness formula specifies, that the evaluation measure is let free to evolve, and we obtain co-evolution of the expressed behavior and the individual evolvable fitness formula. In an isolated environment a generalist behavior emerges when organisms have a fixed fitness formula, and a specialist behavior emerges when organisms have individual evolvable fitness formulae. A population diversification analysis shows, that almost all organisms in a population in an isolated environment converge towards the same behavioral strategy, while we find, that competition can act to provide population diversification in populations of organisms in a shared environment. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #177993. 	[Title] Generalist and Specialist Behavior Due to Individual Energy Extracting Abilities. [Abstract] The emergence of generalist and specialist behavior in populations of neural networks is studied. Energy extracting ability is included as a property of an organism. In artificial life simulations with organisms living in an environment, the fitness score can be interpreted as the combination of an organisms behavior and the ability of the organism to extract energy from potential food sources distributed in the environment. The energy extracting ability is viewed as an evolvable trait of organisms a particular organism's mechanisms for extracting energy from the environment and, therefore, it is not fixed and decided by the researcher. Simulations with fixed and evolvable energy extracting abilities show that the energy extracting mechanism, the sensory apparatus, and the behavior of organisms may co-evolve and be co-adapted. The results suggest that populations of organisms evolve to be generalists or specialists due to individual energy extracting abilities. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1110515. 	[Title] Environmental Effects on Minimal Behaviors in the Minimat World [Abstract] The structure of an environment affects the behaviors of the organisms that have evolved in it. How is that structure to be described, and how can its behavioral consequences be explained and predicted? We aim to establish initial answers to these questions by simulating the evolution of very simple organisms in simple environments with different structures. Our artificial creatures, called "minimats," have neither sensors nor memory and behave solely by picking amongst the actions of moving, eating, reproducing, and sitting, according to an inherited probability distribution. Our simulated environments contain only food (and multiple minimats) and are structured in terms of their spatial and temporal food density and the patchiness with which the food appears. Changes in these environmental parameters affect the evolved behaviors of minimats in different ways, and all three parameters are of importance in describing the minimat world. One of the most useful behavioral strategies that evolves is "looping" movement, which allows minimats-despite their lack of internal state-to match their behavior to the temporal (and spatial) structure of their environment. Ultimately we find that minimats construct their own environments through their individual behaviors, making the study of the impact of global environment structure on individual behavior much more complex. Please summarise all the given information.

Paper #286513 has the title `Specialization under Social Conditions in Shared Environments  
` and the abstract `Specialist and generalist behaviors in populations of artificial neural networks are studied. A genetic algorithm is used to simulate evolution processes, and thereby to develop neural network control systems that exhibit specialist or generalist behaviors according to the fitness formula. With evolvable fitness formulae the evaluation measure is let free to evolve, and we obtain a co-evolution of the expressed behavior and the individual evolvable fitness formula. The use of evolvable fitness formulae lets us work in a dynamic fitness landscape, opposed to most work, that traditionally applies to static fitness landscapes, only. The role of competition in specialization is studied by letting the individuals live under social conditions in the same, shared environment and directly compete with each other. We find, that competition can act to provide population diversification in populations of organisms with individual evolvable fitness formulae.
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Question: Which category should Paper #286513 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #286513 has the title `Specialization under Social Conditions in Shared Environments  
` and the abstract `Specialist and generalist behaviors in populations of artificial neural networks are studied. A genetic algorithm is used to simulate evolution processes, and thereby to develop neural network control systems that exhibit specialist or generalist behaviors according to the fitness formula. With evolvable fitness formulae the evaluation measure is let free to evolve, and we obtain a co-evolution of the expressed behavior and the individual evolvable fitness formula. The use of evolvable fitness formulae lets us work in a dynamic fitness landscape, opposed to most work, that traditionally applies to static fitness landscapes, only. The role of competition in specialization is studied by letting the individuals live under social conditions in the same, shared environment and directly compete with each other. We find, that competition can act to provide population diversification in populations of organisms with individual evolvable fitness formulae.
`.
Question: Which category should Paper #286513 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